research-article

On Easiest Functions for Somatic Contiguous Hypermutations And Standard Bit Mutations

Authors:

Pietro S. Oliveto,

Christine ZargesAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1399 - 1406

https://doi.org/10.1145/2739480.2754799

Published: 11 July 2015 Publication History

Abstract

Understanding which function classes are easy and which are hard for a given algorithm is a fundamental question for the analysis and design of bio-inspired search heuristics. A natural starting point is to consider the easiest and hardest functions for an algorithm. For the (1+1)EA using standard bit mutation it is well known that OneMax is an easiest function with unique optimum while Trap is a hardest.

In this paper we extend the analysis of easiest function classes to the contiguous somatic hypermutation (CHM) operator used in artificial immune systems. We define a function MinBlocks and prove that it is an easiest function for the (1+1)EA using CHM, presenting both a runtime and a fixed budget analysis. Since MinBlocks is, up to a factor of 2, a hardest function for standard bit mutations, we consider the effects of combining both operators into a hybrid algorithm. We show that an easiest function for the hybrid algorithm is not just a trivial weighted combination of the respective easiest functions for each operator. Nevertheless, by combining the advantages of both operators, the hybrid algorithm has optimal asymptotic performance on both OneMax and MinBlocks.

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Cited By

Nallaperuma SNeumann FSudholt D(2017)Expected fitness gains of randomized search heuristics for the traveling salesperson problemEvolutionary Computation10.1162/evco_a_0019925:4(673-705)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1162/evco_a_00199
Corus DHe JJansen TOliveto PSudholt DZarges C(2017)On Easiest Functions for Mutation Operators in Bio-Inspired OptimisationAlgorithmica10.1007/s00453-016-0201-478:2(714-740)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s00453-016-0201-4
Bulanova NBuzdalova ABuzdalov MNeumann FSutton A(2016)Fitness-Dependent Hybridization of Clonal Selection Algorithm and Random Local SearchProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2908996(5-6)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908961.2908996
Show More Cited By

Index Terms

On Easiest Functions for Somatic Contiguous Hypermutations And Standard Bit Mutations
1. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

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Published: 11 July 2015

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European Commission

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GECCO '15

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SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Nallaperuma SNeumann FSudholt D(2017)Expected fitness gains of randomized search heuristics for the traveling salesperson problemEvolutionary Computation10.1162/evco_a_0019925:4(673-705)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1162/evco_a_00199
Corus DHe JJansen TOliveto PSudholt DZarges C(2017)On Easiest Functions for Mutation Operators in Bio-Inspired OptimisationAlgorithmica10.1007/s00453-016-0201-478:2(714-740)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s00453-016-0201-4
Bulanova NBuzdalova ABuzdalov MNeumann FSutton A(2016)Fitness-Dependent Hybridization of Clonal Selection Algorithm and Random Local SearchProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2908996(5-6)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908961.2908996
Doerr BJansen TZarges C(2016)Artificial Immune Systems can Beat Evolutionary Algorithms in Combinatorial OptimisationProceedings of the 2016 on Genetic and Evolutionary Computation Conference - GECCO '1610.1145/2908812.2908892(77-84)Online publication date: 2016
https://doi.org/10.1145/2908812.2908892
Qian CTang KZhou Z(2016)Selection Hyper-heuristics Can Provably Be Helpful in Evolutionary Multi-objective OptimizationParallel Problem Solving from Nature – PPSN XIV10.1007/978-3-319-45823-6_78(835-846)Online publication date: 31-Aug-2016
https://doi.org/10.1007/978-3-319-45823-6_78

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