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Merge pull request #1365 from WebFuzzing/feature/one-plus-lambda-lambda
OnePlusLambdaLambda GA
2 parents e151e08 + 37b221a commit 3b24190

12 files changed

Lines changed: 1125 additions & 2 deletions

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

Lines changed: 34 additions & 1 deletion
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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 // These 3 are still work-in-progress
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StandardGA, MonotonicGA, SteadyStateGA, BreederGA, CellularGA, OnePlusLambdaLambdaGA // 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.")
@@ -2742,6 +2742,39 @@ class EMConfig {
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@Min(0.0)
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var elitesCount: Int = 1
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// Cellular GA neighborhood configuration
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enum class CGANeighborhoodModel {
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RING, L5, C9, C13
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}
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@Experimental
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@Cfg("Cellular GA: neighborhood model (RING, L5, C9, C13)")
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var cgaNeighborhoodModel: CGANeighborhoodModel = CGANeighborhoodModel.RING
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/**
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* Breeder GA: truncation fraction to build parents pool P'. Range (0,1].
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*/
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@Experimental
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@PercentageAsProbability
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@Cfg("Breeder GA: fraction of top individuals to keep in parents pool (truncation).")
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var breederTruncationFraction: Double = 0.5
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/**
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* Breeder GA: minimum number of parents to keep after truncation.
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*/
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@Experimental
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@Min(2.0)
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@Cfg("Breeder GA: minimum number of individuals in parents pool after truncation.")
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var breederParentsMin: Int = 2
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/**
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* OnePlusLambdaLambda GA: number of offspring (λ).
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*/
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@Experimental
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@Min(1.0)
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@Cfg("1+(λ,λ) GA: number of offspring (λ) per generation")
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var onePlusLambdaLambdaOffspringSize: Int = 4
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@Experimental
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@Cfg("In REST APIs, when request Content-Type is JSON, POJOs are used instead of raw JSON string. " +
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"Only available for JVM languages")

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

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@@ -645,6 +645,15 @@ 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.BreederGA ->
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Key.get(object : TypeLiteral<BreederGeneticAlgorithm<GraphQLIndividual>>() {})
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EMConfig.Algorithm.CellularGA ->
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Key.get(object : TypeLiteral<CellularGeneticAlgorithm<GraphQLIndividual>>() {})
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EMConfig.Algorithm.OnePlusLambdaLambdaGA ->
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Key.get(object : TypeLiteral<OnePlusLambdaLambdaGeneticAlgorithm<GraphQLIndividual>>() {})
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else -> throw IllegalStateException("Unrecognized algorithm ${config.algorithm}")
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}
@@ -670,6 +679,15 @@ 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.BreederGA ->
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Key.get(object : TypeLiteral<BreederGeneticAlgorithm<RPCIndividual>>() {})
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EMConfig.Algorithm.CellularGA ->
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Key.get(object : TypeLiteral<CellularGeneticAlgorithm<RPCIndividual>>() {})
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EMConfig.Algorithm.OnePlusLambdaLambdaGA ->
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Key.get(object : TypeLiteral<OnePlusLambdaLambdaGeneticAlgorithm<RPCIndividual>>() {})
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else -> throw IllegalStateException("Unrecognized algorithm ${config.algorithm}")
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}
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}
@@ -694,6 +712,15 @@ 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.BreederGA ->
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Key.get(object : TypeLiteral<BreederGeneticAlgorithm<WebIndividual>>() {})
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EMConfig.Algorithm.CellularGA ->
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Key.get(object : TypeLiteral<CellularGeneticAlgorithm<WebIndividual>>() {})
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EMConfig.Algorithm.OnePlusLambdaLambdaGA ->
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Key.get(object : TypeLiteral<OnePlusLambdaLambdaGeneticAlgorithm<WebIndividual>>() {})
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else -> throw IllegalStateException("Unrecognized algorithm ${config.algorithm}")
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}
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}
@@ -728,6 +755,15 @@ 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.BreederGA ->
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Key.get(object : TypeLiteral<BreederGeneticAlgorithm<RestIndividual>>() {})
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EMConfig.Algorithm.CellularGA ->
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Key.get(object : TypeLiteral<CellularGeneticAlgorithm<RestIndividual>>() {})
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EMConfig.Algorithm.OnePlusLambdaLambdaGA ->
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Key.get(object : TypeLiteral<OnePlusLambdaLambdaGeneticAlgorithm<RestIndividual>>() {})
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731767
else -> throw IllegalStateException("Unrecognized algorithm ${config.algorithm}")
732768
}
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}

‎core/src/main/kotlin/org/evomaster/core/search/algorithms/BreederGeneticAlgorithm.kt‎

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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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import kotlin.math.max
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/**
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* Breeder Genetic Algorithm (BGA)
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*
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* Differences vs Standard GA:
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* - Uses truncation selection to build a parents pool P'.
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* - At each step, creates two offspring from two random parents in P',
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* then randomly selects ONE of the 2 offspring to add to the next population.
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*/
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class BreederGeneticAlgorithm<T> : AbstractGeneticAlgorithm<T>() where T : Individual {
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override fun getType(): EMConfig.Algorithm {
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return EMConfig.Algorithm.BreederGA
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}
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override fun searchOnce() {
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beginGeneration()
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frozenTargets = archive.notCoveredTargets()
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val n = config.populationSize
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// Elitism base for next generation
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val nextPop = formTheNextPopulation(population)
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// Build parents pool P' by truncation on current population
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val parentsPool = buildParentsPoolByTruncation(population)
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while (nextPop.size < n) {
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beginStep()
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val p1 = randomness.choose(parentsPool)
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val p2 = randomness.choose(parentsPool)
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// Work on copies
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val o1 = p1.copy()
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val o2 = p2.copy()
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if (randomness.nextBoolean(config.xoverProbability)) {
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xover(o1, o2)
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}
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if (randomness.nextBoolean(config.fixedRateMutation)) {
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mutate(o1)
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}
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if (randomness.nextBoolean(config.fixedRateMutation)) {
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mutate(o2)
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}
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// Randomly pick one child to carry over
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var chosen = o1
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if (!randomness.nextBoolean()) {
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chosen = o2
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}
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nextPop.add(chosen)
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if (!time.shouldContinueSearch()) {
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endStep()
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break
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}
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endStep()
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}
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population.clear()
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population.addAll(nextPop)
68+
endGeneration()
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}
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private fun buildParentsPoolByTruncation(pop: List<WtsEvalIndividual<T>>): List<WtsEvalIndividual<T>> {
72+
if (pop.isEmpty()) {
73+
return pop
74+
}
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val sorted = pop.sortedByDescending { score(it) }
77+
val k = max(config.breederParentsMin, (sorted.size * config.breederTruncationFraction).toInt())
78+
return sorted.take(k)
79+
}
80+
}

‎core/src/main/kotlin/org/evomaster/core/search/algorithms/CellularGeneticAlgorithm.kt‎

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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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/**
8+
* Cellular Genetic Algorithm (cGA).
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*
10+
* The population is organized on a virtual grid defined by a neighborhood model
11+
* (e.g., ring, 5-linear, compact 9, compact 13). For each cell i in the current
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* generation, the algorithm performs a localized evolutionary step restricted to
13+
* i's neighborhood:
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*
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* 1) Build i's neighborhood according to the configured model (with wrap-around).
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* 2) Select two parents via tournament restricted to that neighborhood.
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* 3) Create two offspring and apply crossover with probability
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* `xoverProbability`.
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* 4) Retain the best offspring by fitness and apply mutation with probability
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* `fixedRateMutation`.
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* 5) Replace the individual at position i with the best between the mutated offspring
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* and the current individual at i (local elitism).
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*
24+
* One pass over all cells produces the next population. The process repeats until the
25+
* global stopping criterion is met.
26+
*/
27+
class CellularGeneticAlgorithm<T> : AbstractGeneticAlgorithm<T>() where T : Individual {
28+
29+
override fun getType(): EMConfig.Algorithm {
30+
return EMConfig.Algorithm.CellularGA
31+
}
32+
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override fun searchOnce() {
34+
beginGeneration()
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// Freeze targets for current generation
36+
frozenTargets = archive.notCoveredTargets()
37+
38+
val n = population.size
39+
val next: MutableList<WtsEvalIndividual<T>> = mutableListOf()
40+
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for (i in 0 until n) {
42+
beginStep()
43+
val p = population[i]
44+
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val neighbors = getNeighborhood(i)
46+
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// Cellular tournament selection within neighborhood
48+
val x = neighborhoodTournament(neighbors)
49+
val y = neighborhoodTournament(neighbors)
50+
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val o1 = x.copy()
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val o2 = y.copy()
53+
if (randomness.nextBoolean(config.xoverProbability)) {
54+
xover(o1, o2)
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}
56+
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val o: WtsEvalIndividual<T> = if (score(o1) >= score(o2)) o1 else o2
58+
59+
if (randomness.nextBoolean(config.fixedRateMutation)) {
60+
mutate(o)
61+
}
62+
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val bestLocal: WtsEvalIndividual<T> = if (score(o) >= score(p)) o else p
64+
next.add(bestLocal)
65+
endStep()
66+
}
67+
68+
population.clear()
69+
population.addAll(next)
70+
endGeneration()
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}
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/**
74+
* Runs tournament selection restricted to a neighborhood subset.
75+
*/
76+
private fun neighborhoodTournament(neighbors: List<WtsEvalIndividual<T>>): WtsEvalIndividual<T> {
77+
val sel = selectionStrategy.select(neighbors, config.tournamentSize, randomness, ::score)
78+
observers.forEach { it.onSelection(sel) }
79+
return sel
80+
}
81+
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/**
83+
* Returns the neighborhood list for a given index based on the configured model.
84+
* The returned list always includes the current cell ("center") and neighbors,
85+
* in the order customary for the chosen model.
86+
*/
87+
private fun getNeighborhood(index: Int): List<WtsEvalIndividual<T>> {
88+
val model = config.cgaNeighborhoodModel
89+
val neighborhood = Neighborhood<T>(population.size)
90+
if (model == EMConfig.CGANeighborhoodModel.RING) {
91+
return neighborhood.ringTopology(population, index)
92+
}
93+
if (model == EMConfig.CGANeighborhoodModel.L5) {
94+
return neighborhood.linearFive(population, index)
95+
}
96+
if (model == EMConfig.CGANeighborhoodModel.C9) {
97+
return neighborhood.compactNine(population, index)
98+
}
99+
return neighborhood.compactThirteen(population, index)
100+
}
101+
}
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‎core/src/main/kotlin/org/evomaster/core/search/algorithms/NeighborModels.kt‎

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package org.evomaster.core.search.algorithms
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/**
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* An interface that defines the four neighbourhood models used with the cGA
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*/
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interface NeighborModels<T> {
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fun ringTopology(collection: List<T>, position: Int): List<T>
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fun linearFive(collection: List<T>, position: Int): List<T>
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fun compactNine(collection: List<T>, position: Int): List<T>
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fun compactThirteen(collection: List<T>, position: Int): List<T>
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}
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