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Merge pull request #1368 from WebFuzzing/feature/mu-plus-lambda
Mu Plus Lambda EA
2 parents b8de7e7 + 654f6f7 commit bdad980

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‎core/src/main/kotlin/org/evomaster/core/EMConfig.kt‎

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@@ -1162,7 +1162,7 @@ class EMConfig {
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enum class Algorithm {
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DEFAULT, SMARTS, MIO, RANDOM, WTS, MOSA, RW,
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StandardGA, MonotonicGA, SteadyStateGA, BreederGA, CellularGA, OnePlusLambdaLambdaGA, MuLambdaEA // GA variants still work-in-progress.
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StandardGA, MonotonicGA, SteadyStateGA, BreederGA, CellularGA, OnePlusLambdaLambdaGA, MuLambdaEA, MuPlusLambdaEA // GA variants still work-in-progress.
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}
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@Cfg("The algorithm used to generate test cases. The default depends on whether black-box or white-box testing is done.")
@@ -1556,6 +1556,9 @@ class EMConfig {
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@Probability
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var fixedRateMutation = 0.04
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@Cfg("Define the number of offspring (λ) generated per generation in (μ+λ) Evolutionary Algorithm")
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@Min(1.0)
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var muPlusLambdaOffspringSize = 30
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@Cfg("Define the number of offspring (λ) generated per generation in (μ,λ) Evolutionary Algorithm")
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@Min(1.0)
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var muLambdaOffspringSize = 30

‎core/src/main/kotlin/org/evomaster/core/Main.kt‎

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@@ -645,6 +645,9 @@ class Main {
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EMConfig.Algorithm.StandardGA ->
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Key.get(object : TypeLiteral<StandardGeneticAlgorithm<GraphQLIndividual>>() {})
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EMConfig.Algorithm.MuPlusLambdaEA ->
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Key.get(object : TypeLiteral<MuPlusLambdaEvolutionaryAlgorithm<GraphQLIndividual>>() {})
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EMConfig.Algorithm.MuLambdaEA ->
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Key.get(object : TypeLiteral<org.evomaster.core.search.algorithms.MuLambdaEvolutionaryAlgorithm<GraphQLIndividual>>(){})
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EMConfig.Algorithm.BreederGA ->
@@ -681,6 +684,9 @@ class Main {
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EMConfig.Algorithm.RW ->
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Key.get(object : TypeLiteral<RandomWalkAlgorithm<RPCIndividual>>() {})
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EMConfig.Algorithm.MuPlusLambdaEA ->
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Key.get(object : TypeLiteral<MuPlusLambdaEvolutionaryAlgorithm<RPCIndividual>>() {})
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EMConfig.Algorithm.MuLambdaEA ->
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Key.get(object : TypeLiteral<org.evomaster.core.search.algorithms.MuLambdaEvolutionaryAlgorithm<RPCIndividual>>(){})
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@@ -716,6 +722,9 @@ class Main {
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EMConfig.Algorithm.RW ->
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Key.get(object : TypeLiteral<RandomWalkAlgorithm<WebIndividual>>() {})
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EMConfig.Algorithm.MuPlusLambdaEA ->
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Key.get(object : TypeLiteral<MuPlusLambdaEvolutionaryAlgorithm<WebIndividual>>() {})
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EMConfig.Algorithm.MuLambdaEA ->
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Key.get(object : TypeLiteral<org.evomaster.core.search.algorithms.MuLambdaEvolutionaryAlgorithm<WebIndividual>>(){})
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@@ -760,6 +769,8 @@ class Main {
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EMConfig.Algorithm.RW ->
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Key.get(object : TypeLiteral<RandomWalkAlgorithm<RestIndividual>>() {})
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EMConfig.Algorithm.MuPlusLambdaEA ->
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Key.get(object : TypeLiteral<MuPlusLambdaEvolutionaryAlgorithm<RestIndividual>>() {})
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EMConfig.Algorithm.MuLambdaEA ->
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Key.get(object : TypeLiteral<org.evomaster.core.search.algorithms.MuLambdaEvolutionaryAlgorithm<RestIndividual>>(){})
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‎core/src/main/kotlin/org/evomaster/core/search/algorithms/MuPlusLambdaEvolutionaryAlgorithm.kt‎

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@@ -0,0 +1,50 @@
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package org.evomaster.core.search.algorithms
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import org.evomaster.core.EMConfig
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import org.evomaster.core.search.Individual
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import org.evomaster.core.search.algorithms.wts.WtsEvalIndividual
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/**
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* (μ + λ) Evolutionary Algorithm.
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* Population P of size μ is evolved by generating λ offspring via mutation of each parent,
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* then selecting the best μ individuals from parents ∪ offspring.
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*/
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class MuPlusLambdaEvolutionaryAlgorithm<T> : AbstractGeneticAlgorithm<T>() where T : Individual {
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override fun getType(): EMConfig.Algorithm = EMConfig.Algorithm.MuPlusLambdaEA
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override fun searchOnce() {
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beginGeneration()
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// Freeze targets for current generation
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frozenTargets = archive.notCoveredTargets()
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val mu = config.populationSize
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val lambda = config.muPlusLambdaOffspringSize
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val offspring: MutableList<WtsEvalIndividual<T>> = mutableListOf()
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// For each parent, generate λ/μ offspring by mutation (rounded up)
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val perParent = lambda / mu
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for (p in population) {
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for (i in 0 until perParent) {
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beginStep()
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val o = p.copy()
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if (randomness.nextBoolean(config.fixedRateMutation)) {
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mutate(o)
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}
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offspring.add(o)
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endStep()
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}
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}
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// Select best μ from parents ∪ offspring
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val merged = (population + offspring).sortedByDescending { score(it) }
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val next = merged.take(mu).map { it.copy() }.toMutableList()
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population.clear()
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population.addAll(next)
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endGeneration()
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}
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}
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‎core/src/test/kotlin/org/evomaster/core/search/algorithms/MuPlusLambdaEvolutionaryAlgorithmTest.kt‎

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package org.evomaster.core.search.algorithms
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import com.google.inject.Injector
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import com.google.inject.Key
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import com.google.inject.Module
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import com.google.inject.TypeLiteral
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import com.netflix.governator.guice.LifecycleInjector
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import org.evomaster.core.BaseModule
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import org.evomaster.core.EMConfig
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import org.evomaster.core.TestUtils
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import org.evomaster.core.search.algorithms.observer.GARecorder
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import org.evomaster.core.search.algorithms.onemax.OneMaxIndividual
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import org.evomaster.core.search.algorithms.onemax.OneMaxModule
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import org.evomaster.core.search.algorithms.onemax.OneMaxSampler
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import org.evomaster.core.search.service.ExecutionPhaseController
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import org.junit.jupiter.api.Assertions.*
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import org.junit.jupiter.api.BeforeEach
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import org.junit.jupiter.api.Test
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class MuPlusLambdaEvolutionaryAlgorithmTest {
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private lateinit var injector: Injector
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@BeforeEach
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fun setUp() {
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injector = LifecycleInjector.builder()
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.withModules(* arrayOf<Module>(OneMaxModule(), BaseModule()))
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.build().createInjector()
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}
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// Verifies that the (μ+λ) EA can find the optimal solution for the OneMax problem
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@Test
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fun testMuPlusLambdaEAFindsOptimum() {
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TestUtils.handleFlaky {
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val ea = injector.getInstance(
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Key.get(object : TypeLiteral<MuPlusLambdaEvolutionaryAlgorithm<OneMaxIndividual>>() {})
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)
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val config = injector.getInstance(EMConfig::class.java)
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config.populationSize = 5
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config.muPlusLambdaOffspringSize = 10
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config.maxEvaluations = 10000
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config.stoppingCriterion = EMConfig.StoppingCriterion.ACTION_EVALUATIONS
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val epc = injector.getInstance(ExecutionPhaseController::class.java)
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epc.startSearch()
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val solution = ea.search()
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epc.finishSearch()
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assertEquals(1, solution.individuals.size)
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assertEquals(OneMaxSampler.DEFAULT_N.toDouble(), solution.overall.computeFitnessScore(), 0.001)
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}
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}
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// Edge Case: CrossoverProbability=0 and MutationProbability=1
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@Test
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fun testNoCrossoverWhenProbabilityZero_MuPlusEA() {
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TestUtils.handleFlaky {
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val ea = injector.getInstance(
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Key.get(object : TypeLiteral<MuPlusLambdaEvolutionaryAlgorithm<OneMaxIndividual>>() {})
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)
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val rec = GARecorder<OneMaxIndividual>()
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ea.addObserver(rec)
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val config = injector.getInstance(EMConfig::class.java)
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config.gaSolutionSource = EMConfig.GASolutionSource.POPULATION
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config.maxEvaluations = 100_000
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config.stoppingCriterion = EMConfig.StoppingCriterion.ACTION_EVALUATIONS
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config.populationSize = 5
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config.muPlusLambdaOffspringSize = 10 // divisible by mu
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config.xoverProbability = 0.0 // disable crossover
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config.fixedRateMutation = 1.0 // force mutation
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ea.setupBeforeSearch()
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ea.searchOnce()
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val nextPop = ea.getViewOfPopulation()
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// population remains of size mu in (μ+λ) EA
80+
assertEquals(config.populationSize, nextPop.size)
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// crossover disabled (and not used by this EA anyway)
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assertEquals(0, rec.xoCalls.size)
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// λ offspring mutated
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assertEquals(config.muPlusLambdaOffspringSize, rec.mutated.size)
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}
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}
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// Edge Case: MutationProbability=0 and CrossoverProbability=1
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@Test
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fun testNoMutationWhenProbabilityZero_MuPlusEA() {
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TestUtils.handleFlaky {
93+
val ea = injector.getInstance(
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Key.get(object : TypeLiteral<MuPlusLambdaEvolutionaryAlgorithm<OneMaxIndividual>>() {})
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)
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val rec = GARecorder<OneMaxIndividual>()
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ea.addObserver(rec)
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val config = injector.getInstance(EMConfig::class.java)
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config.gaSolutionSource = EMConfig.GASolutionSource.POPULATION
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config.maxEvaluations = 100_000
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config.stoppingCriterion = EMConfig.StoppingCriterion.ACTION_EVALUATIONS
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config.populationSize = 5
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config.muPlusLambdaOffspringSize = 10 // divisible by mu
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config.xoverProbability = 1.0 // force crossover (not used in this EA)
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config.fixedRateMutation = 0.0 // disable mutation
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ea.setupBeforeSearch()
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ea.searchOnce()
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val nextPop = ea.getViewOfPopulation()
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// population remains of size mu in (μ+λ) EA
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assertEquals(config.populationSize, nextPop.size)
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// crossovers are not used in (μ+λ) EA
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assertEquals(0, rec.xoCalls.size)
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// mutations disabled
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assertEquals(0, rec.mutated.size)
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}
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}
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// One iteration properties: population size, best-µ selection, mutation count
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@Test
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fun testNextGenerationIsTheBestMuOfParentsUnionOffspring() {
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TestUtils.handleFlaky {
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val ea = injector.getInstance(
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Key.get(object : TypeLiteral<MuPlusLambdaEvolutionaryAlgorithm<OneMaxIndividual>>() {})
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)
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val rec = GARecorder<OneMaxIndividual>()
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ea.addObserver(rec)
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val config = injector.getInstance(EMConfig::class.java)
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config.populationSize = 5
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config.muPlusLambdaOffspringSize = 10 // divisible by mu -> perParent = 2
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config.xoverProbability = 0.0 // not used in (µ+λ)
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config.fixedRateMutation = 1.0 // force mutation on all offspring
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// initialize population and snapshot parents
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ea.setupBeforeSearch()
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val parents = ea.getViewOfPopulation().toList()
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// run a single generation
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ea.searchOnce()
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val finalPop = ea.getViewOfPopulation()
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val mu = config.populationSize
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// 1) population size remains µ
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assertEquals(mu, finalPop.size)
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// 2) final population equals best-µ of parents ∪ offspring (compare scores)
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val offspring = rec.mutated.toList()
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val expectedScores = (parents + offspring)
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.map { ea.score(it) }
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.sortedDescending()
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.take(mu)
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val finalScores = finalPop
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.map { ea.score(it) }
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.sortedDescending()
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assertEquals(expectedScores, finalScores)
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// 3) with fixedRateMutation=1, mutations equal number of created offspring
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val perParent = config.muPlusLambdaOffspringSize / config.populationSize
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val expectedMutations = perParent * config.populationSize
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assertEquals(expectedMutations, rec.mutated.size)
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}
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}
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}
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‎docs/options.md‎

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@@ -68,7 +68,7 @@ There are 3 types of options:
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|`addPreDefinedTests`| __Boolean__. Add predefined tests at the end of the search. An example is a test to fetch the schema of RESTful APIs. *Default value*: `true`.|
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|`addTestComments`| __Boolean__. Add summary comments on each test. *Default value*: `true`.|
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|`advancedBlackBoxCoverage`| __Boolean__. Apply more advanced coverage criteria for black-box testing. This can result in larger generated test suites. *Default value*: `true`.|
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|`algorithm`| __Enum__. The algorithm used to generate test cases. The default depends on whether black-box or white-box testing is done. *Valid values*: `DEFAULT, SMARTS, MIO, RANDOM, WTS, MOSA, RW, StandardGA, MonotonicGA, SteadyStateGA, BreederGA, CellularGA, OnePlusLambdaLambdaGA, MuLambdaEA`. *Default value*: `DEFAULT`.|
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|`algorithm`| __Enum__. The algorithm used to generate test cases. The default depends on whether black-box or white-box testing is done. *Valid values*: `DEFAULT, SMARTS, MIO, RANDOM, WTS, MOSA, RW, StandardGA, MonotonicGA, SteadyStateGA, BreederGA, CellularGA, OnePlusLambdaLambdaGA, MuLambdaEA, MuPlusLambdaEA`. *Default value*: `DEFAULT`.|
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|`allowInvalidData`| __Boolean__. When generating data, allow in some cases to use invalid values on purpose. *Default value*: `true`.|
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|`appendToStatisticsFile`| __Boolean__. Whether should add to an existing statistics file, instead of replacing it. *Default value*: `false`.|
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|`archiveAfterMutationFile`| __String__. Specify a path to save archive after each mutation during search, only useful for debugging. *DEBUG option*. *Default value*: `archive.csv`.|
@@ -164,6 +164,7 @@ There are 3 types of options:
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|`minimizeTimeout`| __Int__. Maximum number of minutes that will be dedicated to the minimization phase. A negative number mean no timeout is considered. A value of 0 means minimization will be skipped, even if minimize=true. *Default value*: `5`.|
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|`minimumSizeControl`| __Int__. Specify minimum size when bloatControlForSecondaryObjective. *Constraints*: `min=0.0`. *Default value*: `2`.|
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|`muLambdaOffspringSize`| __Int__. Define the number of offspring (λ) generated per generation in (μ,λ) Evolutionary Algorithm. *Constraints*: `min=1.0`. *Default value*: `30`.|
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|`muPlusLambdaOffspringSize`| __Int__. Define the number of offspring (λ) generated per generation in (μ+λ) Evolutionary Algorithm. *Constraints*: `min=1.0`. *Default value*: `30`.|
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|`mutatedGeneFile`| __String__. Specify a path to save mutation details which is useful for debugging mutation. *DEBUG option*. *Default value*: `mutatedGeneInfo.csv`.|
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|`nameWithQueryParameters`| __Boolean__. Specify if true boolean query parameters are included in the test case name. Used for test case naming disambiguation. Only valid for Action based naming strategy. *Default value*: `true`.|
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|`namingStrategy`| __Enum__. Specify the naming strategy for test cases. *Valid values*: `NUMBERED, ACTION`. *Default value*: `ACTION`.|

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