Moscato, P. (1999), Memetic Algorithms: A Short Introduction. In D. Corne, M. Dorigo & F. Glover (eds), New Ideas in Optimization, Mc Graw Hill, pp 219-234.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....is the use of local search in EMO algorithms. Hybridization of evolutionary algorithms with local search has already been investigated for single objective optimization problems in many studies (e.g. 14] 15] Such a hybrid algorithm is often referred to as a memetic algorithm. See Moscato [16] for an introduction to this field and [17] 19] for recent developments. The hybridization with local search for multiobjective optimization was first implemented in [20] 21] as a multiobjective genetic local search (MOGLS) algorithm where a scalar fitness function with random weights was used ....

P. Moscato, "Memetic algorithms: A short introduction," in D. Corne, F. Glover, and M. Dorigo (eds.), New Ideas in Optimization, McGraw-Hill, pp. 219-234, Maidenhead, 1999.

.... that is less expensive, can find the right number of hidden units without so much interference from the user, and is as accurate as that of Fogel et al. 8, 9] In this light, the objective of this paper is to present a Memetic (i.e. evolutionary algorithms (EAs) augmented with local search [20]) pareto artificial neural networks (MPANNs) algorithm for BCD. This approach is found to be as accurate as the method of Fogel et al. but with much lower computational cost. The rest of the paper is organized as follows: In Section 2, background materials are covered followed by an explanation of ....

P. Moscato. Memetic algorithms: a short introduction. In D. Corne, M. Dorigo, and F. Glover, editors, New ideas in optimization, pages 219--234. McGraw-Hill, London, 1999.

....units and the generalization error, the EANN problem is in e#ect a MOP. It is, therefore, natural to raise the question of why not applying a multi objective approach to EANN. The objective of this paper is to present a Memetic (i.e. evolutionary algorithms (EAs) augmented with local search [18]) Pareto Artificial Neural Networks (MPANN) The rest of the paper is organized as follows: In Section 2, background materials are covered followed by an explanation of the methods in Section 3. Results are discussed in Section 4 and conclusions are drawn in Section 5. 2 Background Materials In ....

P. Moscato. Memetic algorithms: a short introduction. In D. Corne, M. Dorigo, and F. Glover, editors, New ideas in optimization, pages 219--234. McGraw-Hill, 1999.

....to a population, that is, a set of candidate solutions, and allowing some limited interaction between the population elements. Such extensions have strong similarities to well known population based search metaphors such as evolutionary algorithms [18, 19] and, in particular, memetic algorithms [6, 20]. Population based extensions of ILS were independently proposed in [21, 22] and applied to the TSP and the QAP. In particular, Stutzle reported results for different levels of interaction among the individual ILS search threads: no interaction, replace worst and population ILS. No interaction, ....

....indicated that the single trajectory ILS algorithm for the MPTTP shows stagnation behaviour, something that was not observed for the SPTTP. To overcome this stagnation behaviour, we developed population based extensions of the ILS algorithms. We also considered the extension to memetic algorithms [6, 20] by including a recombination operator that is applied with some probability to pairs of solutions and returns a new solution that combines properties of both parents . We developed four population based ILS algorithms, ranging from the no interaction scheme mentioned in Section 3 to a ....

Moscato, P.: Memetic algorithms: A short introduction. In Corne, D., Dorigo, M., Glover, F., eds.: New Ideas in Optimization. McGraw Hill, London, UK (1999) 219--234

....are approximate algorithms which encompass and combine constructive methods, local search strategies, local optima escaping strategies and population based search. They include, but are not restricted to: Tabu Search [26] Simulated Annealing [38] Evolutionary Computation [8] Memetic Algorithms [45], Scatter Search [25] Ant Colony Optimization [17, 10] GRASP [19] Iterated Local Search [54, 53] and Variable Neighborhood Search [28] Surveys and current research on metaheuristics can be found in [59, 10, 6] In this paper, we revisit metaheuristics in a multiagent perspective and provide a ....

....based on the algorithm origins. There are natureinspired algorithms, like Genetic Algorithms and Ant Algorithms, and non nature inspired ones like Tabu Search and Iterated Local Search. This classi cation is quite rough for two main reasons: i) recent hybrid algorithms, e.g. memetic algorithms [45], do not completely t neither class or, in a sense, they t both at the same time, ii) this classi cation is not helpful to compare algorithms. Another way to classify metaheuristics is di erentiating them between single and population heuristics. The rst are commonly called trajectory methods ....

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P. Moscato. Memetic algorithms: A short introduction. In F. Glover D. Corne and M. Dorigo, editors, New Ideas in Optimization. McGraw-Hill, 1999.

....A compact edge set encoding and special initialization and variation operators that include a local improvement heuristic are applied. In this way, the space of locally optimal solutions is searched only. The approach belongs to the broader class of so called local search based memetic algorithms [8]. Based on this algorithm for E2AUG, the memetic algorithm for V2AUG presented in this article has been developed. Major di#erences lie in the underlying data structures (e.g. the now necessary block cut tree) the preprocessing, and the local improvement algorithm. While it is relatively easy to ....

P. Moscato. Memetic algorithms: A short introduction. In D. Corne et al., editors, New Ideas in Optimization, pages 219--234. McGraw Hill, 1999.

....the selected one. 4.1. Stochastic local improvement As a central element of the EA, a stochastic local improvement procedure is used during initialization, recombination, and mutation. In this way, the proposed EA searches the space of locally optimal solutions only; compare memetic algorithms [13], which follow a similar principle. Our stochastic local improvement removes redundant edges from a feasible edge set S in a random way until S becomes locally edge minimal, i.e. no further edges can be removed without making S infeasible by introducing bridges into G S . Figure 3 shows the ....

P. Moscato. Memetic algorithms: A short introduction. In D. C. et al., editor, New Ideas in Optimization, pages 219--234. McGraw Hill, Berkshire, England, 1999.

....almost never fail to improve on the current algorithm s performance. The model of Davis, particularly where the current algorithm is used for initialization and to actually make successive transformations of the solution vector is more akin to the notion of a memetic algorithm as described in [Mos99] and [HM99] Both formulations fall under what we consider to be a hybrid evolutionary approach, for the purposes of this section. Memetic algorithms, for our purposes, mean evolutionary approaches that include some iterative local search element to improve the solutions found by recombination ....

....element to improve the solutions found by recombination and or mutation. Memetic algorithms, albeit under different names, have been remarkably successful on a wide range of NP hard problems including TSP, graph colouring, set covering and many others. For an extensive list with references see [Mos99]. The theory behind the approach is not well founded,however [Mos99] Nonetheless, some interesting and potentially general results have been found. Knowles [KC00] empirically demonstrates that searching a landscape with a hybrid approach which mixes hillclimbing, recombination and mutation ....

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P. Moscato. Memetic algorithms: A short introduction. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 219--234. McGrawHill, Maidenhead, Berkshire, England, UK, 1999.

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P. Moscato. Memetic Algorithms: A short introduction. Chapter 14 of New Ideas in Optimization, D. Corne, M. Dorigo, and F. Glover (Eds.), McGraw-Hill, pp. 219-234, 1999.

....International Conference. Porto, Portugal, July 16 20, 2001 MIC 2001 4th Metaheuristics International Conference 2 code, which are pretty much a concern when addressing larger instances. 2 Memetic Algorithm template A generic skeleton for Local Search based MAs was proposed by Moscato in [M99]. The pseudocode given below is generic enough to encompass most implementations which can be found in the literature. Local Search based Memetic Algorithm begin initializePopulation Pop using FirstPop( foreach i # Pop do i : Local Search Engine(i) foreach i # Pop do Evaluate(i) ....

P. Moscato. Memetic Algorithms: A short introduction. Chapter 14 of New Ideas in Optimization, D. Corne, M. Dorigo, and F. Glover (Eds.), McGraw-Hill, pp. 219-234, 1999.

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Moscato, P. (1999), Memetic Algorithms: A Short Introduction. In D. Corne, M. Dorigo & F. Glover (eds), New Ideas in Optimization, Mc Graw Hill, pp 219-234.

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Moscato, P.: Memetic algorithms: A short introduction. In Corne, D., Dorigo, M., Glover, F., eds.: New Ideas in Optimization. McGraw-Hill, London UK (1999) 219--234

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P. Moscato, "Memetic Algorithms: A Short Introduction," in New Ideas in Optimization, (D. Corne, M. Dorigo, and F. Glover, eds.), ch. 14, pp. 219--234, McGraw-Hill, London, 1999.

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P. Moscato. Memetic algorithms: A short introduction. In D. Corne and et al., editors, New Ideas in Optimization, pages 219--234. McGraw Hill, England, 1999.

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P. Moscato, \Memetic Algorithms: A Short Introduction," in New Ideas in Optimization, edited by D. Corne, M. Dorigo, and F. Glover, 219-234, (McGraw{Hill, London, 1999).

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P. Moscato. Memetic algorithms: A short introduction. In D. Corne and et al., editors, New Ideas in Optimization, pages 219--234. McGraw Hill, England, 1999.

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P. Moscato. Memetic algorithms: A short introduction. In D. Corne, F. Glover, and M. Dorigo, editors, New Ideas in Optimization. McGraw-Hill, 1999.

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P. Moscato, Memetic algorithms: A short introduction, in New Ideas in Optimization, (eds.) Corne D., Dorigo M. and Glover F., McGraw-Hill, London UK, 1999, pp. 219-34.

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P. Moscato, \Memetic Algorithms: A Short Introduction," in New Ideas in Optimization, (D. Corne, M. Dorigo, and F. Glover, eds.), ch. 14, pp. 219-234, McGraw-Hill, London, 1999.

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Moscato P (1999) Memetic algorithms: a short introduction. In New ideas in optimization (Edited by Corne D, Dorigo M and Glover F), pp.219-234. McGraw-Hill, London.

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P. Moscato. Memetic algorithms: A short introduction. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 219-234. McGraw-Hill, 1999.

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P. Moscato, "Memetic algorithms: A short introduction," in New Ideas in Optimization, D. Corne, F. Glover, and M. Dorigo, Eds. McGraw-Hill, 1999.

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P. Moscato. Memetic algorithms: A short introduction. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 219{ 234. McGraw Hill, London, UK, 1999.

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P. Moscato, "Memetic algorithms: A short introduction," in D. Corne, F. Glover, and M. Dorigo (eds.), New Ideas in Optimization, McGraw-Hill, pp. 219-234, Maidenhead, 1999.

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P. Moscato. Memetic Algorithms: A Short Introduction. In M. Dorigo et al. eds, New Ideas in Opt.. McGraw-Hill, 1999.

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