Genetic Algorithms and Genetic Programming for Multiscale Modeling

Sep 12, 20074 likes1,545 views

Effective and efficient multiscale modeling is essential to advance both the science and synthesis in a wide array of fields such as physics, chemistry, materials science, biology, biotechnology and pharmacology. This study investigates the efficacy and potential of using genetic algorithms for multiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately.

Genetic Algorithms and Genetic Programming for Multiscale Modeling

Digital predistortion (DPD) is a baseband signal processing technique that corrects for impairments in RF power amplifiers (PAs). These impairments cause out-of-band emissions or spectral regrowth and in-band distortion, which correlate with an increased bit error rate (BER). Wideband signals with a high peak-to-average ratio, are more susceptible to these unwanted effects. So to reduce these impairments, this paper proposes the modeling of the digital predistortion for the power amplifier using GSA algorithm.

Detection and Classification in Hyperspectral Images using Rate Distortion an...Pioneer Natural Resources

This document summarizes an experiment that compares two methods of bit allocation for compressing hyperspectral imagery using JPEG2000: 1) the traditional high bit rate quantizer approach and 2) the rate distortion optimal (RDO) approach. The experiment shows that both methods perform well at relatively low bit rates, achieving over 96% classification accuracy. However, at very low bit rates, the RDO approach outperforms the high bit rate quantizer approach, achieving 90% accuracy at 0.0375 bpppb compared to less than 90% for the high bit rate method. The RDO approach also achieves lower mean squared error than the high bit rate quantizer approach.

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This document proposes an improved Quantum behaved electro-magnetism algorithm particle swarm optimization (QEMAPSO) approach to solve the economic load dispatch (ELD) problem. The objective is to minimize the total generation cost while considering constraints like generator limits, transmission losses, and valve point effects. It formulates the ELD problem and describes the QEMA-PSO algorithm which uses QEMA to determine an initial solution that is then optimized using PSO. Results on a 6-generator IEEE 30-bus test system show that the proposed QEMA-PSO approach improves upon other methods like genetic algorithm and dance bee colony optimization in minimizing costs and emissions.

Hybrid Particle Swarm Optimization for Multi-objective Reactive Power Optimiz...IDES Editor

This paper presents a new hybrid particle swarm optimization (HPSO) method for solving multi-objective real power optimization problem. The objectives of the optimization problem are to minimize the losses and to maximize the voltage stability margin. The proposed method expands the original GA and PSO to tackle the mixed –integer nonlinear optimization problem and achieves the voltage stability enhancement with continuous and discrete control variables such as generator terminal voltages, tap position of transformers and reactive power sources. A comparison is made with conventional, GA and PSO methods for the real power losses and this method is found to be effective than other methods. It is evaluated on the IEEE 30 and 57 bus test system, and the simulation results show the effectiveness of this approach for improving voltage stability of the system.

2006 ssiaiPioneer Natural Resources

The document discusses optimizing rate allocation for compressing hyperspectral images using JPEG2000. It proposes using the discrete wavelet transform instead of the Karhunen-Loeve transform for decorrelation due to lower computational complexity. A mixed model is used for rate distortion optimal bit allocation instead of experimentally obtained rate distortion curves. Comparisons show the mixed model approach results in lower mean squared error than traditional bit allocation schemes, while having lower implementation complexity than prior methods.

22). smlevel energy eff-dynamictaskschedngPoornima_Rajanna

This document proposes algorithms for dynamic task scheduling on a DVS system to minimize total system energy consumption. It presents two algorithms: 1) duSYS, which uses optimal speed setting and limited preemption to reduce device standby energy. 2) duSYS_PC, which further reduces preemptions compared to duSYS to achieve additional energy savings. The algorithms achieve up to 43% energy savings compared to prior work and up to 30% savings compared to a CPU-energy efficient algorithm, showing their effectiveness in minimizing total system energy.

Molecular Simulation to build models for enzyme induced fit MinSung Kim

Hybrid Particle Swarm Optimization for Multi-objective Reactive Power Optimiz...IDES Editor

This paper proposes a hybrid particle swarm optimization (HPSO) method to solve the multi-objective reactive power dispatch problem of minimizing transmission losses and maximizing voltage stability. The HPSO method expands on PSO and genetic algorithms to handle mixed integer and nonlinear optimization problems with both continuous and discrete control variables. The method is evaluated on the IEEE 30-bus and 57-bus test systems. Results show the HPSO approach improves voltage stability compared to conventional, genetic algorithm, and particle swarm optimization methods.

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The document describes an optimal design project to maximize the specific energy absorption of thin-walled square tube structures using LS-OPT. Crash simulations were performed in LS-DYNA to determine the internal energy and crushing force for different tube thickness values. A kriging meta-model and genetic algorithm in LS-OPT were used to optimize the thickness for maximum specific energy absorption subject to a crushing force constraint. The results showed specific energy absorption greater than predicted for thicknesses from 1.93-2 mm, with a maximum crushing force below the constraint. However, only two iterations were completed, so the optimal thickness was not fully converged.

Dual technique of reconfiguration and capacitor placement for distribution sy...IJECEIAES

Radial Distribution System (RDS) suffer from high real power losses and lower bus voltages. Distribution System Reconfiguration (DSR) and Optimal Capacitor Placement (OCP) techniques are ones of the most economic and efficient approaches for loss reduction and voltage profile improvement while satisfy RDS constraints. The advantages of these two approaches can be concentrated using of both techniques together. In this study two techniques are used in different ways. First, the DSR technique is applied individually. Second, the dual technique has been adopted of DSR followed by OCP in order to identify the technique that provides the most eﬀective performance. Three optimization algorithms have been used to obtain the optimal design in individual and dual technique. Two IEEE case studies (33bus, and 69 bus) used to check the effectiveness of proposed approaches. A Direct Backward Forward Sweep Method (DBFSM) has been used in order to calculate the total losses and voltage of each bus. Results show the capability of the proposed dual technique using Modified Biogeography Based Optimization (MBBO) algorithm to find the optimal solution for significant loss reduction and voltage profile enhancement. In addition, comparisons with literature works done to show the superiority of proposed algorithms in both techniques.

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This document summarizes a research paper that presents a reconfigurable multiplier circuit that can dynamically adjust its precision, voltage, and frequency to minimize power consumption based on workload. It incorporates multiple smaller precision multipliers that can operate independently or in parallel. Razor flip-flops and dynamic voltage scaling are used to aggressively lower the voltage while ensuring correctness. Experimental results showed the design achieved up to 86.3% power reduction with only 11.1% area overhead compared to a fixed-width multiplier.

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Device parameter optimization of Silicon Germanium HBT for THz applicationsPradeep Kumar

Now-a-days SiGe HBTs are surpassing even the fastest III-V production devices in the high–speed orbit. The state-of-art in simulation of silicon germanium semiconductor devices is presented in this paper. A comprehensive course of action to model the device parameter characterization of High Frequency 0.1μm SiGe HBT is depicted which is based on the technique of direct parameter extraction. With the help of S-, Y- and Z- parameters, the equivalent circuit parameters have been extracted in a faultless approach. The intrinsic and the extrinsic elements of model are obtained using a direct extraction method that assists to find out the base resistance from the Z- parameters. In this, the issues entailed in simultaneous optimization of fmax and fT and related optimization of base resistance as well as junction capacitance are addressed. The device characteristics of the SiGe HBT are found much advance to those of III–V semiconductor devices. A corroboration of objective validity of the modeling scheme and the extraction of parameter is accomplished in the form of S-parameters. These results have been validated using a viable numerical device simulator ATLAS from Silvaco International.

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This paper has been studied an important issue of energy-efficient scheduling on multi-FPGA systems. The main challenges are integral allocation, reconfiguration overhead and exclusiveness and energy minimization with deadline constraint. To tackle these challenges, based on the theory of dynamic programming, we have designed and implemented an energy-efficient scheduling on multi-FPGA systems. Differently, we have presented a MLPF algorithm for task placement on FPGAs. Finally, the experimental results have demonstrated that the proposed algorithm can successfully accommodate all tasks without violation of the deadline constraint. Additionally, it gains higher energy reduction 13.3% and 26.3% than that of Particle Swarm Optimization and fully balanced algorithm, respectively.

N03430990106ijceronline

An Improved Particle Swarm Optimization for Proficient Solving of Unit Commit...IDES Editor

This paper presents a new approach to solving the short-term unit commitment problem using an improved Particle Swarm Optimization (IPSO). The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. PSO, which happens to be a Global Optimization technique for solving Unit Commitment Problem, operates on a system, which is designed to encode each unit’s operating schedule with regard to its minimum up/down time. In this, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here, each schedule is formed by committing all the units according to their initial status (“flat start”). Here the parents are obtained from a predefined set of solution’s i.e. each and every solution is adjusted to meet the requirements. Then, a random decommitment is carried out with respect to the unit’s minimum down times. A thermal Power System in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different power systems consist of 10, 26, 34 generating units. Numerical results are shown comparing the cost solutions and computation time obtained by using the IPSO and other conventional methods like Dynamic Programming (DP), Legrangian Relaxation (LR) in reaching proper unit commitment.

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This document discusses atomistic simulations of defective boron nitride (BN) nanofillers. It begins with background on BN, noting its hexagonal structure and exceptional properties. Molecular dynamics simulations are introduced as a way to model defective BN at the atomic level. The document then details simulations performed with the Tersoff potential to study the effects of defects on BN mechanical behavior. It is found that introducing defects changes the failure morphology from brittle to more ductile. Future work plans include simulating BN with different potential parameters and defect types to further understand their effects.

Speed Conrol of Separately Excited dc Motor using Fuzzy TechniqueIDES Editor

This paper presents the speed control of a separately excited DC motor using Fuzzy Logic Control (FLC). The Fuzzy Logic Controller designed in this study applies the required control voltage based on motor speed error (e) and its change (ce). The performance of the driver system was evaluated through digital simulations using Simulink. The simulation results show that the control with FLC outperforms PI control in terms of overshoot, steady state error and rise time.

Efficient realization-of-an-adfe-with-a-new-adaptive-algorithmCemal Ardil

This document presents a new adaptive algorithm for an adaptive decision feedback equalizer (ADFE) that has lower computational complexity than existing algorithms. The proposed block-based normalized least mean square (BBNLMS) algorithm with set-membership filtering for the ADFE achieves similar bit error rate performance and convergence speed as conventional algorithms like set-membership normalized least mean square (SM-NLMS), but with significantly fewer computations. Simulation results show the new algorithm provides comparable equalization performance to SM-NLMS while realizing about a 70% reduction in computational operations, especially at high signal-to-noise ratios, making it suitable for high-speed decision feedback equalization applications.

COMPARISION OF TONE-MAPPING ALGORITHM BASED ON STRUCTURAL FIDELITY AND STATIS...Sumadeep Juvvalapalem

1) The document presents a comparison of tone-mapping algorithms based on structural fidelity and statistical naturalness. It proposes a quality assessment algorithm that combines a structural similarity index with naturalness measures of intensity statistics. 2) Tone-mapping operators are used to convert high dynamic range images to low dynamic range for display on standard monitors. The proposed method assesses quality by analyzing both structural fidelity and naturalness. 3) Adaptive fusion tone-mapping is used to remove losses and display high quality HDR images on LDR displays. Structural fidelity and statistical naturalness are calculated and parameters are automatically tuned based on thresholds.

Optimal Synthesis of Array Pattern for Concentric Circular Antenna Array Usin...IDES Editor

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1. The document describes an application of the unscented Kalman filter (UKF) for state estimation in sonar signal processing. 2. It provides background on linear and nonlinear state estimation techniques, including the Kalman filter, extended Kalman filter, unscented Kalman filter, and particle filter. 3. As an example, it models target tracking using bearing-only measurements, where the target is assumed to move at constant course and velocity. The UKF is used to estimate the target state parameters in the presence of measurement noise.

Biogeography Based Optimization Approach for Optimal Power Flow Problem Consi...IDES Editor

This paper presents a novel Biogeography Based Optimization (BBO) algorithm for solving multi-objective constrained optimal power flow problems in power system. In this paper, the feasibility of the proposed algorithm is demonstrated for IEEE 30-bus system with three different objective functions and it is compared to other well established population based optimization techniques. A comparison of simulation results reveals better solution quality and computation efficiency of the proposed algorithm over particle swarm optimization (PSO), Real Coded Genetic algorithm (RGA) for the global optimization of multiobjective constrained OPF problems.

Fast and accurate reaction dynamics via multiobjective genetic algorithm opti...kknsastry

The document describes research into using multiobjective genetic algorithms to optimize semiempirical potentials for fast and accurate reaction dynamics simulations. The researchers developed a method to tune semiempirical parameters using a limited set of ab initio calculations to better describe excited state potential energy surfaces. They found that multiobjective optimization was able to find globally accurate potential energy surfaces more efficiently than weighted single-objective optimizations. Analysis of the optimized parameter sets showed they produced stable and physically reasonable results.

Eurogen vArmando Vieira

This document describes a multi-objective evolutionary algorithm that uses artificial neural networks to approximate fitness functions in order to reduce the number of exact function evaluations. The algorithm runs the evolutionary algorithm for an initial number of generations to collect a training dataset. It then trains a neural network on this dataset. The evolutionary algorithm continues running for additional generations, using the neural network to approximate some or all of the fitness function evaluations. The neural network approximation error is monitored, and the evolutionary algorithm switches back to using exact function evaluations when the error becomes too high. This process repeats until an acceptable Pareto front is found. The method was tested on benchmark multi-objective test functions and showed a 20-40% reduction in the number of exact function evaluations needed

Gradient-Based Multi-Objective Optimization TechnologyeArtius, Inc.

Multi-Gradient Analysis (MGA), and two multi-objective optimization methods based on MGA are presented: Multi-Gradient Explorer (MGE), and Multi Gradient Pathfinder (MGP) methods. Dynamically Dimensioned Response Surface Method (DDRSM) for dynamic reduction of task dimension and fast estimation of gradients is also disclosed. MGE and MGP are based on the MGA’s ability to analyze gradients and determine the area of simultaneous improvement (ASI) for all objective functions. MGE starts from a given initial point, and approaches Pareto frontier sequentially by stepping into the ASI area until a Pareto optimal point is obtained. MGP starts from a Pareto-optimal point, and steps along the Pareto surface in the direction that allows for improvement on a subset of the objective functions with higher priority. DDRSM works for optimization tasks with virtually any number (up to thousands) of design variables, and requires just 5-7 model evaluations per Pareto optimal point for the MGE and MGP algorithms regardless of task dimension. Both algorithms are designed to optimize computationally expensive models, and are able to optimize models with dozens, hundreds, and even thousands of design variables.

Evolutionary-driven Optimization in Computational ChemistryUniversity of Zurich

Evolution-based Reaction Path FollowingUniversity of Zurich

The document discusses using evolutionary algorithms to optimize transition state structures. It introduces the objectives of detecting transition states efficiently through simulations. Evolutionary algorithms are described as using populations of individuals that evolve over time through selection, crossover, and mutation operators to adapt to their environment. The fitness function measures this adaptation by identifying transition state structures as energy maxima along reaction paths. Several questions are raised about the encoding of variables and meaning of perturbations in the algorithms. Alternative approaches like sphere-based reaction path following are proposed, which use evolutionary algorithms to locate meaningful optima on energy surfaces to find reaction paths.

eArtius HMGE Algorithm Applied to Optimization Tasks with 10,000 Design Varia...eArtius, Inc.

eArtius has developed a multi-objectve optimization technology which is not sensitive to the model dimension because it performs optimization in a sub-space of the design space related to the most significant design variables. All non-significant design variables are dynamically recognized in runtime, and simply ignored.Thus, eArtius algorithms are equally efficient for low-dimensional and high-dimensional tasks.

Xin Yao: "What can evolutionary computation do for you?"ieee_cis_cyprus

Evolutionary computation techniques like genetic programming and evolutionary algorithms can be used for adaptive optimization, data mining, and machine learning. They have been successfully applied to problems like modeling galaxy distributions, material modeling, constraint handling, dynamic optimization, multi-objective optimization, and ensemble learning. While evolutionary computation has had many real-world applications, challenges remain in improving theoretical foundations, scalability to large problems, dealing with dynamic and uncertain environments, and developing the ability to learn from previous optimization experiences.

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This paper analyzes the scalability of the population size required in XCS to maintain niches that are infrequently activated. Facetwise models have been developed to predict the effect of the imbalance ratio—ratio between the number of instances of the majority class and the minority class that are sampled to XCS—on population initialization, and on the creation and deletion of classifiers of the minority class. While theoretical models show that, ideally, XCS scales linearly with the imbalance ratio, XCS with standard configuration scales exponentially. The causes that are potentially responsible for this deviation from the ideal scalability are also investigated. Specifically, the inheritance procedure of classifiers’ parameters, mutation, and subsumption are analyzed, and improvements in XCS’s mechanisms are proposed to effectively and efficiently handle imbalanced problems. Once the recommendations are incorporated to XCS, empirical results show that the population size in XCS indeed scales linearly with the imbalance ratio.

Substructrual surrogates for learning decomposable classification problems: i...kknsastry

This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model that represents salient interactions between attributes for a given data, (2) a surrogate model which provides a functional approximation of the output as a function of attributes, and (3) a classification model which predicts the class for new inputs. The structural model is used to infer the functional form of the surrogate and its coefficients are estimated using linear regression methods. The classification model uses a maximally-accurate, least-complex surrogate to predict the output for given inputs. The structural model that yields an optimal classification model is searched using an iterative greedy search heuristic. Results show that the proposed method successfully detects the interacting variables in hierarchical problems, group them in linkages groups, and build maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and have also shed light on several improvements to enhance the capabilities of the proposed method.

Let's get ready to rumble redux: Crossover versus mutation head to head on ex...kknsastry

This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of non-uniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for deterministic exponentially-scaled additively separable, problems, the BB-wise mutation is more efficient than crossover yielding a speedup of Θ(l logl), where l is the problem size. For the noisy exponentially-scaled problems, the outcome depends on whether scaling on noise is dominant. When scaling dominates, mutation is more efficient than crossover yielding a speedup of Θ(l logl). On the other hand, when noise dominates, crossover is more efficient than mutation yielding a speedup of Θ(l).

Automated alphabet reduction with evolutionary algorithms for protein structu...kknsastry

This paper focuses on automated procedures to reduce the dimensionality of protein structure prediction datasets by simplifying the way in which the primary sequence of a protein is represented. The potential benefits of this procedure are faster and easier learning process as well as the generation of more compact and human-readable classifiers. The dimensionality reduction procedure we propose consists on the reduction of the 20-letter amino acid (AA) alphabet, which is normally used to specify a protein sequence, into a lower cardinality alphabet. This reduction comes about by a clustering of AA types accordingly to their physical and chemical similarity. Our automated reduction procedure is guided by a fitness function based on the Mutual Information between the AA-based input attributes of the dataset and the protein structure feature that being predicted. To search for the optimal reduction, the Extended Compact Genetic Algorithm (ECGA) was used, and afterwards the results of this process were fed into (and validated by) BioHEL, a genetics-based machine learning technique. BioHEL used the reduced alphabet to induce rules for protein structure prediction features. BioHEL results are compared to two standard machine learning systems. Our results show that it is possible to reduce the size of the alphabet used for prediction from twenty to just three letters resulting in more compact, i.e. interpretable, rules. Also, a protein-wise accuracy performance measure suggests that the loss of accuracy accrued by this substantial alphabet reduction is not statistically significant when compared to the full alphabet.

Analyzing probabilistic models in hierarchical BOA on traps and spin glasseskknsastry

The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although Bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem.

Modeling selection pressure in XCS for proportionate and tournament selectionkknsastry

Modeling XCS in class imbalances: Population size and parameter settingskknsastry

On Extended Compact Genetic Algorithmkknsastry

In this study we present a detailed analysis of the extended compact genetic algorithm (ECGA). Based on the analysis, empirical relations for population sizing and convergence time have been derived and are compared with the existing relations. We then apply ECGA to a non-azeotropic binary working fluid power cycle optimization problem. The optimal power cycle obtained improved the cycle efficiency by 2.5% over that existing cycles, thus illustrating the capabilities of ECGA in solving real-world problems.

Silicon Cluster Optimization Using Extended Compact Genetic Algorithmkknsastry

This paper presents an efficient cluster optimization algorithm. The proposed algorithm uses extended compact genetic algorithm (ECGA), one of the competent genetic algorithms (GAs) coupled with Nelder-Mead simplex local search. The lowest energy structures of silicon clusters with 4-11 atoms have been successfully predicted. The minimum population size and total number of function (potential energy of the cluster) evaluations required to converge to the global optimum with a reliability of 96% have been empirically determined and are O(n4.2) and O(n8.2) respectively. The results obtained indicate that the proposed algorithm is highly reliable in predicting globally optimal structures. However, certain efficiency techniques have to be employed for predicting structures of larger clusters to reduce the high computational cost due to function evaluation.

A Practical Schema Theorem for Genetic Algorithm Design and Tuningkknsastry

This paper develops the theory that can enable the design of genetic algorithms and choose the parameters such that the proportion of the best building blocks grow. A practical schema theorem has been used for this purpose and its ramification for the choice of selection operator and parameterization of the algorithm is explored. In particular stochastic universal selection, tournament selection, and truncation selection schemes are employed to verify the results. Results agree with the schema theorem and indicate that it must be obeyed in order to ascertain sustained growth of good building blocks. The analysis suggests that schema theorem alone is insufficient to guarantee the success of a selectorecombinative genetic algorithm.

On the Supply of Building Blockskknsastry

This study addresses the issue of building-block supply in the initial population. Facetwise models for supply of a single building block as well as for supply of all schemata in a partition have been developed. An estimate for the population size required to ensure the presence of all raw building blocks has been derived using these facetwise models. The facetwise models and the population-sizing estimate are verified with computational results.

Don't Evaluate, Inheritkknsastry

This paper studies fitness inheritance as an efficiency enhancement technique for genetic and evolutionary algorithms. Convergence and population-sizing models are derived and compared with experimental results. These models are optimized for greatest speed-up and the optimal inheritance proportion to obtain such a speed-up is derived. Results on OneMax problems show that when the inheritance effects are considered in the population-sizing model, the number of function evaluations are reduced by 20% with the use of fitness inheritance. Results indicate that for a fixed population size, the number of function evaluations can be reduced by 70% using a simple fitness inheritance technique.

Efficient Cluster Optimization Using A Hybrid Extended Compact Genetic Algori...kknsastry

A recent study Sastry and Xiao (2001) proposed a highly reliable cluster optimization algorithm which employed extended compact genetic algorithm (ECGA) along with Nelder-Mead simplex search. This study utilizes an efficiency enhancement technique for the ECGA based cluster optimizer to reduce the population size and the number of function evaluation requirements, yet retaining the high reliability of predicting the lowest energy structure. Seeding of initial population with lowest energy structures of smaller cluster has been employed as the efficiency enhancement technique. Empirical results indicate that the population size and total number of function evaluations scale up with the cluster size are reduced from O(n4.2) and O(n8.2) to O(n0.83) and O(n2.45) respectively.

Modeling Tournament Selection with Replacement Using Apparent Added Noisekknsastry

This paper analyzes the effects of tournament selection with replacement on the convergence time and population sizing for selectorecombinative genetic algorithms. This paper empirically demonstrates that the run duration remains the same and is not affected whether the tournament selection is performed with or without replacement. However, the population size required is more if tournament selection is performed with replacement rather than without replacement to attain the same level of accuracy. An approximate population-sizing model is derived based on apparent added noise for the case of tournament selection with replacement. The proposed model is verified with experimental results.

Evaluation Relaxation as an Efficiency-Enhancement Technique: Handling Varian...kknsastry

This study develops a decision-making strategy for deciding between fitness functions with differing bias values. Simple, yet practical facetwise models are derived to aid the decision-making process. The decision making strategy is designed to provide maximum speed-up and thereby enhance the efficiency of GA search processes. Results indicate that bias can be handled temporally and that significant speed-up values can be obtained.