A multiset genetic algorithm for the optimization of deceptive problems

Manso, António; Correia, Luís

doi:10.1145/2463372.2463471

Cited by 5 publications

(12 citation statements)

References 13 publications

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“…In the results presented in [4] SMuGA showed to be able to find BBs in the chromosome and to translocate them to other positions, by action of parasites. When base MuGA algorithm working on the hosts finds BBs, parasites tend to mimic them (due to the parasite evaluation function) and them being translocated replicate the BBs in other regions of the chromosome.…”

Section: Fig 9 Trap Function With 8 Bitsmentioning

confidence: 86%

“…Recombination is done by uniform crossover with probability 0.6 and mutation, replacement and rescaling are performed by the operator Multiset Wave Mutation (MWM) [4] with roughness set to 2 and thinness set to 3. Replacement is done by Multiset Decimation Replacement (MDR) [4] with tournaments of size 2. We use the Adaptive Decimation operator with population factor set to 2 to control the number of copies of the MI.…”

Section: Methodsmentioning

confidence: 99%

“…The collaboration phase ends with the selection of h hosts from the union of host population, hPop and symbiosis population, sPop. The evolution phase starts with the evolution of the host population hPop using MuGA algorithm [4,12] and the evolution of the parasite population pPop using Algorithm 3 (explained in sec. 4.1 Evolution of Parasites).…”

Section: Algorithmmentioning

confidence: 99%

“…This algorithm has obtained competitive results in difficult optimization problems coded by binary strings and vectors of real numbers [4].…”

Section: Introductionmentioning

confidence: 99%

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Parasite Diversity in Symbiogenetic Multiset Genetic Algorithm

Manso

Correia

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Symbiogenetic MuGA (SMuGA) is a co-evolutionary model exploiting the concept of symbiosis over the Multiset Genetic Algorithm (MuGA). It evolves two species: hosts that represent a solution to the problem, and parasites that represent part-solutions. SMuGA has been proved valuable in the optimization of a variety of deceptive functions. However its performance decreased in large scale difficult problems. This paper presents a new version of SMuGA with improvements centered on the evolution process of the parasites. The most significant advance is provided by using a diversity measure to modulate the evolution of parasites. The algorithm is tested with very good results in deceptive functions with up to 1024 bits. The paper is concluded with an analysis of the advantages and limitations of the approach, and with perspectives for future developments.

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Section: Fig 9 Trap Function With 8 Bitsmentioning

confidence: 86%

Section: Methodsmentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

“…This algorithm has obtained competitive results in difficult optimization problems coded by binary strings and vectors of real numbers [4].…”

Section: Introductionmentioning

confidence: 99%

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Parasite Diversity in Symbiogenetic Multiset Genetic Algorithm

Manso

Correia

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…The replacement operator selects which individuals will continue in the evolutionary process. A new operator that uses multiset information to replace populations in a steady-state strategy, called multiset decimation replacement (MDR), is presented in Manso and Correia (2013). The operator must maintain the genetic diversity in the main population so that the genetic operators can circumvent local optima and avoid premature convergence (Yu and Suganthan 2010;Jayachandran and Corns 2010).…”

Section: Multiset Replacementmentioning

confidence: 99%

Reticulate Evolution

Gontier¹

2015

Interdisciplinary Evolution Research

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The time when only biologists studied evolution has long since passed. Accepting evolution requires us to come to terms with the fact that everything that exists must be the outcome of evolutionary processes. Today, a wide variety of academic disciplines are therefore confronted with evolutionary problems, ranging from physics and medicine, to linguistics, anthropology and sociology. Solving evolutionary problems also necessitates an inter-and transdisciplinary approach, which is why the Modern Synthesis is currently extended to include drift theory, symbiogenesis, lateral gene transfer, hybridization, epigenetics and punctuated equilibria theory.The series Interdisciplinary Evolution Research aims to provide a scholarly platform for the growing demand to examine specific evolutionary problems from the perspectives of multiple disciplines. It does not adhere to one specific academic field, one specific school of thought, or one specific evolutionary theory. Rather, books in the series thematically analyze how a variety of evolutionary fields and evolutionary theories provide insights into specific, well-defined evolutionary problems of life and the socio-cultural domain.Editors-in-chief of the series are Nathalie Gontier and Olga Pombo. The Series is edited from within the Applied Evolutionary Epistemology Lab, more information on the lab is available at http://appeel.fc.ul.pt. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made.Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com) v Although originally a systematic term associated with speciation by hybridization, this book exemplifies "reticulate evolution" as it occurs by mechanisms and processes of symbiosis, symbiogenesis, lateral gene transfer, hybridization or divergence with gene flow, and infectious heredity. These phenomena are currently taking up a prominent role in the evolutionary sciences. Almost on a daily basis, new and fascinat...

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

Correia

Manso

2015

Interdisciplinary Evolution Research

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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-theart 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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A multiset genetic algorithm for the optimization of deceptive problems

Cited by 5 publications

References 13 publications

Parasite Diversity in Symbiogenetic Multiset Genetic Algorithm

Parasite Diversity in Symbiogenetic Multiset Genetic Algorithm

Reticulate Evolution

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

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