Memetic Algorithms

Moscato, Pablo; Cotta, Carlos; Mendes, Alexandre

doi:10.1007/978-3-540-39930-8_3

Cited by 97 publications

(39 citation statements)

References 137 publications

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“…Memetic algorithms (Moscato et al, 2004) mechanism. Memetic algorithms have been demonstrated to be successful over a number of different problem domains, including the MKP (Chu and Beasley, 1998;Özcan and Basaran, 2009).…”

Section: Hyper-heuristic Frameworkmentioning

confidence: 99%

A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem

Drake

Özcan

Burke

2016

Evolutionary Computation

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Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain.

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Section: Hyper-heuristic Frameworkmentioning

confidence: 99%

A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem

Drake

Özcan

Burke

2016

Evolutionary Computation

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“…However typically the GA needs to be tweaked to get the best for each problem. Mahajan and Ali [30] used GP to give better code generation heuristics in Harvard's MachineSUIF compiler. Typically genetic programming is used.…”

Section: Mashups Hyper-heuristics and Multiplicity Computingmentioning

confidence: 99%

“…Perron [30] describes a compositional approach in which search heuristics are termed 'goals'. Perron [30] describes a compositional approach in which search heuristics are termed 'goals'.…”

Section: Related Workmentioning

confidence: 99%

Search-Based Software Engineering

Barros¹,

Labiche²

2015

Lecture Notes in Computer Science

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“…As Kriging is very sensitive to the last steps, while converging to the final accurate optimum, a hill climbing method (local search) is used to fine-tune the optimum. However, while such a two-stage process, i.e., a memetic algorithm [18], performs well with respect to finding the global optimum, it can be prohibitively expensive in higher dimensions. Therefore, another optimisation strategy is used in the blindDACE toolbox which is outlined below.…”

Section: Softwarementioning

confidence: 99%

Blind Kriging: Implementation and performance analysis

Couckuyt

Forrester

Gorissen

et al. 2012

Advances in Engineering Software

116

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When analysing data from computationally expensive simulation codes or process measurements, surrogate modelling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualisation and optimisation. Kriging is a popular surrogate modelling technique for data based on deterministic computer experiments. There exist several types of Kriging, mostly differing in the type of regression function used. Recently a promising new variable selection technique was proposed to identify a regression function in the Kriging framework. In this paper this type of Kriging, i.e., blind Kriging, has been efficiently implemented in Matlab® and has been extended.The implementation is validated and tested on several examples to illustrate the strength and weaknesses of this new, promising modelling technique. It is shown that the performance of blind Kriging is as good as, or better than ordinary Kriging. Though, blind Kriging comes at double the computational cost with respect to ordinary Kriging.

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Memetic Algorithms

Cited by 97 publications

References 137 publications

A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem

A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem

Search-Based Software Engineering

Blind Kriging: Implementation and performance analysis

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