EIG.h File Reference

#include <dignea/core/AbstractEA.h>
#include <dignea/core/Crossover.h>
#include <dignea/core/Mutation.h>
#include <dignea/core/Replacement.h>
#include <dignea/core/Selection.h>
#include <dignea/crossovers/UniformCrossover.h>
#include <dignea/distances/Euclidean.h>
#include <dignea/generator/AbstractDomain.h>
#include <dignea/generator/evaluations/EasyInstances.h>
#include <dignea/generator/evaluations/InstanceFitness.h>
#include <dignea/generator/evaluations/Weighted.h>
#include <dignea/metrics/AverageFitness.h>
#include <dignea/metrics/BestFitness.h>
#include <dignea/mutations/UniformOneMutation.h>
#include <dignea/replacements/EGenerational.h>
#include <dignea/searches/NSFeatures.h>
#include <dignea/searches/NoveltySearch.h>
#include <dignea/selections/BinaryTournamentSelection.h>
#include <dignea/utilities/exceptions/Mismatch.h>
#include <dignea/utilities/random/PseudoRandom.h>
#include <numeric>
#include <vector>

Go to the source code of this file.

Classes
class	EIG< IP, IS, OP, OS >
	Instance Generation Algorithm. Known as Meta-Evolutionary Algorithm (EIG). This algorithm uses a Weighted Fitness Function (WFF) to evaluate how biased and diverse are the instances generated during the evolutionary process. The instances generated should be biased to the performance of the target algorithm in the portfolio but also should show some diversity between them. More...

Detailed Description

Author

Alejandro Marrero (amarr.nosp@m.erd@.nosp@m.ull.e.nosp@m.du.e.nosp@m.s)

Version

0.1

Date

11/3/21.

Copyright

Copyright (c) 2022