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A Multiset Model of Multi-Species Evolution to Solve Big Deceptive Problems

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Reticulate Evolution

Part of the book series: Interdisciplinary Evolution Research ((IDER,volume 3))

Abstract

This chapter presents symbiogenetic multiset genetic algorithm (SMuGA), an integration of symbiogenesis with the multiset genetic algorithm (MuGA). The symbiogenetic approach used here is based on the host–parasite model with the novelty of varying the length of parasites along the evolutionary process. Additionally, it models collaborations between multiple parasites and a single host. To improve efficiency, we introduced proxy evaluation of parasites, which saves fitness function calls and exponentially reduces the symbiotic collaborations produced. Another novel feature consists of breaking the evolutionary cycle into two phases: a symbiotic phase and a phase of independent evolution of both hosts and parasites. SMuGA was tested in optimization of a variety of deceptive functions, with results one order of magnitude better than state-of-the-art symbiotic algorithms. This allowed to optimize deceptive problems with large sizes and showed a linear scaling in the number of iterations to attain the optimum.

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Acknowledgments

The authors thank Mel Todd, Guida Manso, and Nathalie Gontier for the precious revisions that made this text more clear and readable.

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Authors and Affiliations

  1. BioISI—Biosystems and Integrative Sciences Institute, Faculty of Science, University of Lisbon, Campo Grande, 1749-016, Lisbon, Portugal

    Luís Correia & António Manso

  2. Polytechnic Institute of Tomar, Quinta do Contador, Estrada da Serra, 2300-313, Tomar, Portugal

    António Manso

Authors
  1. Luís Correia
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  2. António Manso
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Corresponding author

Correspondence to Luís Correia .

Editor information

Editors and Affiliations

  1. AppEEL—Applied Evolutionary Epistemology Lab, University of Lisbon, Lisbon, Portugal

    Nathalie Gontier

Glossary

Crossover operator

Genetic operator inspired by biological reproduction, where two or more parents exchange genetic information to produce offspring that inherits features from the parents

Coevolution

Simultaneous evolution of two or more species that have a strong ecological relationships among them (predator-prey, mutualism, or parasitic)

Collaboration

Process for creating symbionts through the interaction of two distinct species

Deceptive problems

Problems where the combination of building blocks with low order to form high order building blocks lead to a solution that is not a global optimum

Evolutionary Algorithm

Generic population-based metaheuristic, inspired by biological evolution, which uses genetic inspired operators to evolve solutions to optimization problems that are represented by chromosomes

Genetic Algorithm

Subclass of evolutionary algorithms that evolve a population of individuals, representing solutions to optimization problems, using genetic operators that mimic natural evolution such as selection, crossover, and mutation. Bit string chromosome is the standard

MDR

Multiset Decimation Replacement—multiset selection operator used by MuGA to merge parents and offspring multiset populations

MuGA

Multiset genetic algorithm—evolutionary algorithm that uses multisets to represent populations and genetic operators that take advantage of this representation

Multi-individuals

Set of identical individuals represented by a 2-tuple composed by the chromosome and the number of clones (copies)

Multiset

Collection in which members are allowed to appear more than once. May be formally defined as a set of 2-tuples 〈n, e〉 where n is the number of copies of the element e

Mutation Operator

Analogous to biological mutation, this operator introduces probabilistic random changes in the chromosomes of the individuals

MWM

Multiset wave mutation—multiset mutation operator used by MuGA that applies different probabilities of mutation to clones present in a multi-individual

Rescaling Operator

MuGA genetic operator used to control the number of copies present in Multi-individuals

Selection Operator

Genetic operator that mimics the natural selection of the fittest individuals in the population. In the genetic algorithm context, selection operator is used to choose parents for reproduction and to introduce the offspring in the population

SMuGA

Symbiogenetic multiset genetic algorithm—coevolutionary algorithm that uses symbiogenesis

Symbiogenesis

Evolutionary theory according to which individuals of different species come together to form a new individual (symbiont)

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Correia, L., Manso, A. (2015). A Multiset Model of Multi-Species Evolution to Solve Big Deceptive Problems. In: Gontier, N. (eds) Reticulate Evolution. Interdisciplinary Evolution Research, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-16345-1_11

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