Implementing the Genetic Algorithm in XSLT: PoC

Proof of Concept: SOA Application Composition using the Genetic Algorithm Jim Fuller http://www.ruminate.co.uk http://www.slgchorus.com

Introduction Technical Director / Internet Services Manager for Stuart Lawrence Group companies on-IDLE ltd sponsored 1 st XSLT conference in the world: XSLT UK 2001 along with Dave Pawson co-founder of the EXSLT effort, along with Dave Pawson, Jeni Tennison, Uche Obigu, et al. Technical reviewer and author for now defunct WROX, on books dealing with XML, XSLT and web services

Lecture Overview How we use WS today XSLT and S-expressions Genetic Algorithm refresher Early Genetic Experiments with XSLT Application composition using Genetic Algorithm Conclusions

How we use WS in today's applications Indirectly consume web services via WSDL / UDDI subsequent generation of stub code Direct Consumption of SOAP via manual crafting of HTTP Request headers + SOAP envelope Primary use cases: Integration and Interoperability Emerging use cases: orchestration, higher level business processes, and automated application composition

MVC type architectures are popular Client Tier Presentation Tier Business Tier Integration Tier Resource Tier Data Repository, XML Binding, Persistence Model View Controller External web services Internal web services

WS MVC with the Browser Controller EventHandler SOAPEventHandler Model The Model receives events from the Controller and updates itself sending Data which gets transformed by our view components. View -IE web service client side processing -XSLT templates -CSS -Global.xml -Global.xsl HTTP GET HTTP POST REQUEST Internal web services External web services HTTP RESPONSE Internet Explorer Client

SOA Anchor Stability via web service server : BEA Weblogic, IBM Websphere, Systinet WASP, .NET, ColdFusionMX versioning control of web services Easy to deploy same web service through multiple transports Smooth out learning curve for many of the underlying XML technologies ( SAML ) security integration with underlying PKI Instant solution to some problems Deploy existing code as web service, no need for ‘special’ web service code embedded in your own code

Bazaar not opened yet Currently developers ask how can *I* use them in *my* applications. Web services live behind the firewall and solve integration problems; extraprise. Google, Amazon and Microsoft are all examples of monolithic web services. Many deployed web services are highly specific to a certain problem domain. Who will bind a specific public web service with their precious application ? (Amazon in research pane).

The world of ‘millions of web services’ The question is not ‘how will a developer find a web service?’ but how will a machine find and use the right web service ? How will the developer/machine know it’s the right one ? That its stable, correct version, and it can be trusted… The promise of SOA is real time application composition generating applications or components, based on a set of general evolving criteria

Automatic application composition methods One approach, not linked to any problem domain is to use the Genetic Algorithm…though there are obvious constraints using these methods Random search of the problem domain AI / intelligent Software agent methods

Genetic Algorithm Refresher The Genetic Algorithm ( GA ) is a model of the evolution of a population of artificial individuals. Each individual is a chromosome which contains discrete units of information; in computers this can be a string, binary numbers, etc… . With each generation the best fitness individuals are selected for genetic operations to create new generation The driving force behind the search for new and better solutions is the retention and combination of good partial solutions to a problem

Abridged Genetic Algorithm The Fundamental Theorem of Genetic Algorithms M(H, t) :# of individuals in population 't' with the schema 'H'. f(H) : average fitness of the individuals with the schema 'H'. F : average fitness of the entire population. p1 :probability of the schema being destroyed by crossover. p2 :probability of the schema being destroyed by mutation.

GA operations Reproduction : An individual is perfectly replicated to a new population Crossover ( Recombination ) : Parental material is recombined to create offspring to join new population Mutation : random changes Permutation : reordering Editing : evaluation to a terminal Encapsulation : single indivisible function Decimation : removal of individuals

Genetic Programming Process Step 0 . Create a random initial population of individuals Step 1 . Evaluate the fitness of each individual Step 2 . Select individuals according to their fitness, which will participate in generating offspring (moms+dads) Step 3 . Apply primary and secondary genetic operations to generate new offspring population Step 4 . Repeat the steps 1,2,3, to generate X number of generations Step 5 . choose best fit individual

Symbolic expressions and XSLT XSLT List questions….I originally wanted to solve ‘I want to transform source xml to target xml using XSLT’. Could use generic templates or some other automated process. Vestigial lisp memories of s expressions are similar to xslt / xml: data and programming in one XSLT guru David Carlisle presence at XSLT UK 2001 opened my eyes to functional programming My work with EXSLT defined the limitations of XSLT…which led me to build frameworks to implement complex MVC architectures

(+(* 2 3) 4) evaluates to 10 and symbolic expression looks like; Simplest Lisp Example 3 4 + * 2 Hierarchical computer programs are more expressive then manipulating linear strings

XSLT are also general hierarchical computer programs <xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version=“2.0"> <xsl:template match="a"> <d/> <c/> </xsl:template> </xsl:stylesheet> <d/> <c/> <xsl:template/> <xsl:stylesheet/> There are some differences, e.g. there are a variety of node types within XML

Problem definition Create a GA process that will discover an XSLT program which taken a source.xml generates a target.xml Prototype uses ASF ANT to control the whole process Michael Kay’s excellent SAXON xslt processor, XSLT 2.0 simplified situation by removal of dealing with RTF’s and node-set usage Initially create a simple problem, e.g. that of transforming a source xml into a copy of itself

Source XML <a> <c> <d></d> </c> </a>

Target XML <a> <c> <d></d> </c> </a>

Early Genetic Experiment Step 0 . Randomly generate initial population of xslt documents Step 1 . evaluate fitness using via xml diff of target.xml to result.xml Step 2 . select individuals according to their fitness which can be used by step 3 Step 3 . Apply primary and secondary genetic operations to generate new offspring population from selected individuals Step 4 . Repeat steps 1,2,3, to generate X number of generations Step 5 . choose best fit individual of last generation

M=500, G=51 Parameters Same as raw fitness, approaching 0 is better fitness Standardized fitness One fitness case Fitness Cases Node count on xmldiff patch file difference between result xml and target xml Raw fitness Subset of xslt instructions Function Set <a/> <c/> <d/> Terminal Set Generate an xslt program that transforms source xml into result xml which is equivalent to target xml Objective

Step 0. Generate Initial Population Used IBM xml generator: com.ibm.XMLGenerator.XMLGenerator to generate a population of xslt documents. <?xml version='1.0'?>  <xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0"> <xsl:template match="a"> <c/> <d/> </xsl:template> </xsl:stylesheet>

Avoid ‘early taxonomisation’ No attributes No namespaces No schemas Xmlgenerator DTD defines allowable terminals and functions e.g. xsl:apply-templates, xsl:for-each, xsl:value-of, xsl:copy-of, xsl:choose, xsl:if, xsl:copy. used <a>, , <c>, <d> as the only allowable elements

Ant: generate_initial_population <target name=“generate_initial_population"> <tempfile property="temp.file" prefix="xslt_" suffix=".xsl" destdir="${dirs.src}"/>  <tstamp prefix="start"/>  <property name="xslt.build_number" value="${gen_count}"/>  <java classname="com.ibm.XMLGenerator.XMLGenerator" fork="true" failonerror="false" output="${temp.file}"> <arg value="${xslt.initial_dtd}"/> <arg value="-root"/> <arg value="${xslt.root_node}"/> <arg value="-nodecl"/> <arg value="-f"/> <arg value="1"/> <arg value="-l"/> <arg value="10"/> </java> </target>

Step 1: Evaluate Fitness XSLT generation xslt Source.xml result.xml Target.xml evaluate fitness transformation xml diff Each individual is ranked, by testing xslt program against a source xml

Step 1. evaluate fitness (cont) Could have chosen multiple source and target xml to use in fitness assessment Output of transformation (result.xml) is xmldiff’ed with target xml I used an extremely simple xml diff tool that just output xml patch Converted Diff patch file into a number, which is the number of nodes contained in the patch file

RESULT XML from XSLT individual transformation with SOURCE XML TREEDIFFMERGE DIFFERENCE PATCH <?xml version="1.0" encoding="utf-8"?><root><a> <c> <d/> </c> </a></root> <?xml version="1.0" encoding="UTF-8"?> <diff /> <?xml version="1.0" encoding="utf-8"?><root> <a/><a><a><c/><c><a><d/></a><c/></c></a><a/><c/> <c> <d/> </c> <a/></a><d><a><c/><a/><a/></a><c/></d><c/> </root> <?xml version="1.0" encoding="UTF-8"?> <diff xmlns:diff='http://diff.org'> <diff:copy src="2" dst="1"> <diff:copy src="16" dst="2" /> </diff:copy> </diff> <?xml version="1.0" encoding="UTF-8"?> <root/> <?xml version="1.0" encoding="UTF-8"?> <diff xmlns:diff='http://diff.org'> <diff:insert dst="1"> <a> <c> <d /> </c> </a> </diff:insert> </diff>

XML Diff issues Most diff algorithms are based on a paper published in 1976 by J. W. Hunt and M. D. McIlroy, An Algorithm for Differential File Comparison XML is not just text, it has a structure , text based diff programs do not take this into accordance simple example: <footie/> versus <footie></footie>logically these are equal

Ant: transform_src <target name="transform_src"> <java classname="net.sf.saxon.Transform" fork="true" failonerror="false" output="${current_xslt_file}.xml"> <arg value="${source_xml}"/> <arg value="${current_xslt_file}"/> </java> </target>

Ant: fitness_src <target name="fitness_src"> <java classname="TreeDiffMerge" fork="true" failonerror="false" output="${current_xslt_file}.fitness.xml"> <arg value="-d"/> <arg value="${current_xslt_file}"/> <arg value="${target_xml}"/> </java> </target>

Step 2. Select individuals Probabilistic selection to choose which individuals participate in genetic operation Selected XSLT population Select individuals for genetic operations, based on their fitness

A word on fitness Raw fitness : is the natural representation in terms of the specific problem Standardized fitness : lower the better Adjusted fitness : lies between 0-1 Normalized fitness : lies between 0-1 with sum of fitness values = 1 In our case the lower the number of ‘different’ nodes the better, use standardized fitness

Step 3. Primary Genetic Operations Selected XSLT population New generation Reproduction Individual reproduced into new generation

Step 3. Primary Genetic Operations Selected XSLT population New generation Creates 2 offspring ‘ Mom’ ‘ Dad’ Crossover ( Recombination ) Select parents then crossover creates 2 offspring

Step 3. Primary Genetic Operations Crossover ( Recombination ) ‘ Dad XSLT’ ‘ Mom XSLT’ ‘ offspring xslt’ ‘ offspring xslt’ New generation Swap nodes between selected parent xslt

Step 3. Secondary Genetic Operations Mutation : is a form of random crossover Permutation : Reorganize nodes Editing : evaluate a set of nodes Encapsulation : takes a branch and replaces with 1 indivisible node Decimation : removes individual based on domain specific criteria

Step 3. Secondary Genetic Operations mutation ‘ selected XSLT’ Pick a node and randomly mutate Completely new set of instructions ‘ offspring xslt’

Step 3. Secondary Genetic Operations permutation ‘ selected XSLT’ ‘ offspring xslt’ Permutated node order

Step 3. Secondary Genetic Operations editing ‘ selected XSLT’ ‘ offspring xslt’ Replace node with evaluated expression

Step 3. Secondary Genetic Operations encapsulation ‘ selected XSLT’ ‘ define new function’ Identify useful subtrees and encapsulate by defining new function ‘ XSLT’

Step 3. Secondary Genetic Operations decimation Identify very poor fitness individuals and remove from population <xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0"> </xsl:stylesheet> <xsl:stylesheet/>

Ant: select, perform, and generate new population <target name=" select_crossover_population "> … .xslt transformation selected crossover using xslt </target> <target name=" select_reproduction_population "> … .xslt transformation selected reproduction using xslt </target> <target name=“ perform_genetic_operation "> … .genetic operations were performed using xslt </target> <target name=" generate_new_generation "> … new individuals were copied over to a new directory </target>

Step 4. Generate X populations M= 500, g = 51 Set initial genetic operation probabilities: 90% crossover on selected individuals 10% reproduction on selected individuals 0% secondary operations on selected individuals Define termination criteria if you want an ongoing process until a desired fitness is obtained. Iterate until done

Ant properties <project name=“early_genetic_trial" default="build" basedir=".">  <property name="ant-contrib.jar" location="c:\java\ant-contrib-0.3.jar"/> <taskdef resource="net/sf/antcontrib/antcontrib.properties" classpath="${ant-contrib.jar}"/>  <property name="genetic.pop_size" value=“500"/> <property name="genetic.number_of_generations" value=“51"/> <property name="gen.reproduction_probability" value=".10"/> <property name="gen.recombinate_probability" value=".90"/> <property name="gen.mutation_probability" value=“.0"/> <property name="gen.permuation_probability" value=".0"/> <property name="gen.editing_probability" value=".0"/> <property name="gen.encapsulation_probability" value=".0"/> <property name="gen.decimation_probability" value=".0"/>  <property name=“source_xml" value="c:\_genetic\generate_initial_population\source.xml"/> <property name=“target_xml" value="c:\_genetic\generate_initial_population\target.xml"/>  … contained xslt properties  … contained directory properties  <property name="xslt.report" value="C:\java\jakarta-ant-1.5.1\etc\log.xsl"/>

Simplified Ant Build Target <target name="build" depends="clean, create"> <antcall target=" generate_initial_population "> <param name=“no_of_individuals" value=" ${genetic.pop_size}"/> </antcall> <antcall target=“initiate_genetic_run“/> <antcall target=“report “/> <echo message=“successfully ran genetic run”/> </target>

Results Non-normative results indicate ok processing time e.g. PIII 128 meg RAM approx 7 minutes to solve this problem For simple mapping this was an effective technique Many times best fit were poorly performing XSLT, needed to add criteria to fitness that timed processing time

Ruminations Early success with XSLT approach proved the applicability of GA with xml based technologies Was easy to let people define a source and target xml Issues of speed and efficiency can be addressed later on How could I involve web services into such a process ?

GA Strategies to Consider Could directly apply the genetic algorithm directly with another language; java or c# ? Leverage existing XSLT approach and add SOAP as a new function/terminal via XSLT extension

Enhance existing Prototype augment XSLT approach and introduce web services into terminal/function set Needed a local repository of Web Services to add to existing function set Needed to enhance XSLT with a generic SOAP XSLT Extension which indirectly invokes a web services via WSDL definition Adjust generate initial population to include soap extension

Simple application composition Step 0 . Randomly generate initial population of xslt documents, this is now a 2 stage process to include web services via new function Step 1 . evaluate fitness using via xml diff of target.xml to result.xml Step 2 . select individuals according to their fitness which can be used by step 3 Step 3 . Apply primary and secondary genetic operations to generate new offspring population from selected individuals Step 4 . Repeat steps 1,2,3, to generate X number of generations Step 5 . choose best fit individual of last generation

Web Services Search Engine Long term storage in WSIL format Data was stored in XML Xindice XML Repository Which is accessible via WebDav and HTTP Get Can query using XPATH Harvested by a combination of google, scanning and general web robot techniques

Manual Harvesting of Web Services Google ‘file: wsil’ or inspection.wsil Google ‘file: wsdl’ Scanning common Application Server ports, sending simple SOAP messages Xmethods and general registries Did not want to bind to either WSDL or UDDI….

Simple WSIL example <?xml version="1.0"?> <inspection xmlns="http://schemas.xmlsoap.org/ws/2001/10/inspection/"> <service> <description referencedNamespace="http://schemas.xmlsoap.org/wsdl/" location="http://example.com/stockquote.wsdl" /> </service> </inspection>

WSIL with 2 services <?xml version="1.0"?> <inspection xmlns="http://schemas.xmlsoap.org/ws/2001/10/inspection/" xmlns:wsiluddi="http://schemas.xmlsoap.org/ws/2001/10/inspection/uddi/"> <service> <abstract>A stock quote service with two descriptions</abstract> <description referencedNamespace="http://schemas.xmlsoap.org/wsdl/" location="http://example.com/stockquote.wsdl"/> <description referencedNamespace="urn:uddi-org:api"> <wsiluddi:serviceDescription location="http://www.example.com/uddi/inquiryapi"> <wsiluddi:serviceKey>4FA28580-5C39-11D5-9FCF-BB3200333F79</wsiluddi:serviceKey> </wsiluddi:serviceDescription> </description> </service> <service> <description referencedNamespace="http://schemas.xmlsoap.org/wsdl/" location="ftp://anotherexample.com/tools/calculator.wsdl"/> </service> <link referencedNamespace="http://schemas.xmlsoap.org/ws/2001/10/inspection/" location="http://example.com/moreservices.wsil"/> </inspection>

inspection.wsil at XMETHODS <?xml version='1.0' encoding='UTF-8'?> <inspection xmlns='http://schemas.xmlsoap.org/ws/2001/10/inspection/' xmlns:wsiluddi='http://schemas.xmlsoap.org/ws/2001/10/inspection/uddi/' xmlns:wsilxmethods='http://schemas.xmethods.net/ws/2001/10/inspection/ '> <service> <abstract>Get the Barnes & Noble price by ISBN</abstract> <description referencedNamespace='http://schemas.xmlsoap.org/wsdl/' location='http://www.abundanttech.com/webservices/bnprice/bnprice.wsdl'/> <description referencedNamespace='http://www.xmethods.net/'> <wsilxmethods:serviceDetailPage location='http://www.xmethods.net/ve2/ViewListing.po?key=uuid:C5119582-90AC-51E7-72AA-ED7D8927C9D1'> <wsilxmethods:serviceID>272507</wsilxmethods:serviceID> </wsilxmethods:serviceDetailPage> </description> </service> … .. </inspection>

XSLT Generic SOAP client Created extension function in SAXON, which grew out of a SOAP debugging tool effort ( another talk ! ) Ability to invoke a web service via WSDL and randomly choose web service Web service invocation called during xslt transformation Function prototype: ws:invoke(wsdl,methodname,nodeset)

Example of using a web service in XSLT <xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" xmlns:ws=“http://www.ruminate.co.uk/ws” version=“2.0"> <xsl:template match="a"> <xsl:value-of select=“ws:invoke(‘http://somewsdlfile.wsdl’,’getGUID’,a)”/> </xsl:template> </xsl:stylesheet>

Issues Step 0 generation required additional stages, to introduce ws:invoke combined with WSIL information Encapsulation was applied to xslt statements that contained ws:invoke function, so crossover would not change the statement Always choose 1 st method ( in order ) in WSDL Step 0 consistently generated highly unfit programs, required larger population size Mutation seeding ws:invoke statement vastly speeded up process New timeout factors necessary GA process significantly slowed down due to inclusion of web services GA process was more effective with better fitness evaluation; e.g. ranking fitness consisted of 3 source and targets

M=1000, G=51 Parameters three fitness cases Fitness Cases Node count on xmldiff patch file difference between result xml and target xml Raw fitness Subset of xslt instructions + ws:invoke Function Set <a/>, ( 2 numbers ) Terminal Set Generate an xslt program that multiplies 2 numbers, converts to Celsius and returns number in Chinese Objective

Results Multiply 2 numbers convert to Celsius and result should be in Chinese: average 2 hours Tried a variety of more complicated problems, with many runs never converging to a solution; It is apparent that there is not enough ‘genetic material’ online yet Prototype proved that GA can be applied Assisting GA always speeded up the process Many optimization opportunities

Enhancement Could have used Dimitri Novachtev’s FXSL, though this would have imposed a pure fp viewpoint on process Use UDDI as web services repository Applied GA to ANT or xml pipeline, or even to BPEL, WS-CAF or any xml vocabulary Prototyping with ANT was successful, but eventually will embed in a software framework

The Internet as a maturing Software Framework Inheritance versus composition resuse mechanism Hierarchical versus relational data models Synchronous versus asynchronous Stateful versus stateless Declarative versus OO versus procedural Coarse grained versus RPC versus Object based web services

Conclusion In 5 years time will there be advances in hardware processing to make GA techniques viable? problem domain experts can formulate representation of a problem to be solved using simple xml Coders become farmers Its counter intuitive to generate a million line ‘messy’ program to solve a problem Are there any amends/changes to key specifications that will assist or restrict the GA method ? Thank you, any questions ?

References JOHN R KOZA, Genetic Programming , MIT Press 1992 W3C, SOAP Version 1.2 W3C, XML Version 1 W3C, XSLT Version 2: W3C, WSDL Version 1: WSIL Version 1 J. W. Hunt and M. D. McIlroy , An Algorithm for Differential File Comparison published in 1976 SAXON XSLT PROCESSOR by Michael Kay, http://saxon.sourceforge.net ASF ANT, http://ant.apache.org FXSL, Dimitre Novatchev http://fxsl.sourceforge.net

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