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Two-B or not Two-B?: Design Patterns for Hybrid Metaheuristics

Published: 11 July 2015 Publication History

Abstract

Real world search problems, characterised by nonlinearity, noise and multidimensionality, are often best solved by hybrid algorithms. Techniques embodying different necessary features are triggered at specific iterations, in response to the current state of the problem space. In the existing literature, this alternation is managed either statically (through pre-programmed policies) or dynamically, at the cost of high coupling with algorithm inner representation. We extract two design patterns for hybrid metaheuristic search algorithms, the All-Seeing Eye and the Commentator patterns, which we argue should be replaced by the more flexible and loosely coupled Simple Black Box (Two-B) and Utility-based Black Box (Three-B) patterns that we propose here. We recommend the Two-B pattern for purely fitness based hybridisations and the Three-B pattern for more generic search quality evaluation based hybridisations.

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  • (2018)A pattern-driven solution for designing multi-objective evolutionary algorithmsNatural Computing10.1007/s11047-018-9677-y19:3(481-494)Online publication date: 3-Apr-2018

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cover image ACM Conferences
GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
July 2015
1568 pages
ISBN:9781450334884
DOI:10.1145/2739482
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 ACM 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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Published: 11 July 2015

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  • (2018)A pattern-driven solution for designing multi-objective evolutionary algorithmsNatural Computing10.1007/s11047-018-9677-y19:3(481-494)Online publication date: 3-Apr-2018

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