Real-Coded Memetic Algorithms with Crossover Hill-Climbing

Lozano, Manuel; Herrera, Francisco; Krasnogor, Natalio; Molina, Daniel

doi:10.1162/1063656041774983

Cited by 282 publications

(166 citation statements)

References 27 publications

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“…Although SSGAs are less common than GGAs, different authors [40,41] recommend the use of SSGAs for the design of MAs because they allow the results of LS to be kept in the population from one generation to the next.…”

Section: Steady-state Masmentioning

confidence: 99%

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A memetic algorithm for evolutionary prototype selection: A scaling up approach

García

Cano

Herrera

2008

Pattern Recognition

Self Cite

169

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Prototype selection problem consists of reducing the size of databases by removing samples that are considered noisy or not influential on nearest neighbour classification tasks. Evolutionary algorithms have been used recently for prototype selection showing good results. However, due to the complexity of this problem when the size of the databases increases, the behaviour of evolutionary algorithms could deteriorate considerably because of a lack of convergence. This additional problem is known as the scaling up problem.Memetic algorithms are approaches for heuristic searches in optimization problems that combine a population-based algorithm with a local search. In this paper, we propose a model of memetic algorithm that incorporates an ad hoc local search specifically designed for optimizing the properties of prototype selection problem with the aim of tackling the scaling up problem. In order to check its performance, we have carried out an empirical study including a comparison between our proposal and previous evolutionary and non-evolutionary approaches studied in the literature.The results have been contrasted with the use of non-parametric statistical procedures and show that our approach outperforms previously studied methods, especially when the database scales up. ᭧

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Section: Steady-state Masmentioning

confidence: 99%

“…In order to do this, we have included in the algorithm the Adaptive P LS Mechanism, which is an adaptive fitness-based method that is very simple but it offers good results in Ref. [41]. Indeed, this scheme assigns a LS probability value to each chromosome generated by crossover and mutation, c new :…”

Section: Ssma Model For Ps Problemmentioning

confidence: 99%

A memetic algorithm for evolutionary prototype selection: A scaling up approach

García

Cano

Herrera

2008

Pattern Recognition

Self Cite

169

View full text Add to dashboard Cite

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“…Among many LS methods available in the literature, hill climbing (HC) is a common strategy. In the context of GAs, HC methods may be divided into two ways: crossover-based hill climbing [17,26] and mutation-based hill climbing [16,25]. The basic idea of HC methods is to use stochastic iterative hill climbing as the move acceptance criterion of the search (i.e.…”

Section: Hill Climbingmentioning

confidence: 99%

A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

2008

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Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.

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“…This kind of operators is usually qualified as Lamarckian, referring to the fact that individuals are replaced by the local optima found after applying local search (contrary to the Baldwin model where the local optima is just used to evaluate the individual). Other possibility is using local-search within crossover, e.g., [86] [107]. A similar strategy has been used in non-crossover-based metaheuristics such as ACO [127][124], where local search has been introduced to intensify the search.…”

Section: Classifying Hybrid Metaheuristicsmentioning

confidence: 99%

Parallel Hybrid Metaheuristics

Cotta

Talbi

Alba

2005

Parallel Metaheuristics

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Finding optimal solutions is in general computationally intractable for many combinatorial optimization problems, e.g., those known as NP-hard [54]. The classical approach for dealing with this fact was the use of approximation algorithms, i.e., relaxing the goal from finding the optimal solution to obtaining solutions within some bounded distance from the former [61]. Unfortunately, it turns out that attainable bounds in practice (that is, at a tenable computational cost) are in general too far from the optimum to be useful in many problems. The days in which researchers struggled to slightly tighten worst-case bounds that were anyway far from practical, or in which finding a PTAS (let alone a FPTAS) for a certain problem was considered a whole success are thus swiftly coming to an end. Indeed, two new alternative lines of attack are being currently used to treat these difficulties. On one hand, a new corpus of theory is being built around the notion of fixed-parameter tractability that emanates from the field of parameterized complexity [40][41]. On the other hand, metaheuristics approaches are being increasingly used nowadays. Quoting [27], the philosophy of these latter techniques is "try to obtain probably optimal solutions to your problem, for provably good solutions are overwhelmingly hard to obtain". See also [27][97] for some prospects on the intersection of both fields (parameterized complexity and metaheuristics).Focusing on the latter techniques, metaheuristics approaches can be broadly categorized into two major classes: single-solution search algorithms (also known as trajectory-based or local-search based algorithms), and multiple-solution search algorithms (also-known as population-based or -arguably stretching the term-evolutionary algorithms). Examples of the former class are descent local search (LS) Over the years, interest in metaheuristics has risen considerably among researchers in combinatorial optimization. The flexibility of these techniques makes them prime candidates for tackling both new problems and variants of exiting problems. This fact, i[

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Real-Coded Memetic Algorithms with Crossover Hill-Climbing

Cited by 282 publications

References 27 publications

A memetic algorithm for evolutionary prototype selection: A scaling up approach

A memetic algorithm for evolutionary prototype selection: A scaling up approach

A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

Parallel Hybrid Metaheuristics

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