Conference Paper

Hybrid evolutionary algorithms for constraint satisfaction problems: memetic overkill?

Napier Univ., Edinburgh, UK
DOI: 10.1109/CEC.2005.1554930 Conference: Evolutionary Computation, 2005. The 2005 IEEE Congress on, Volume: 3
Source: IEEE Xplore

ABSTRACT We study a selected group of hybrid EAs for solving CSPs, consisting of the best performing EAs from the literature. We investigate the contribution of the evolutionary component to their performance by comparing the hybrid EAs with their "de-evolutionarised" variants. The experiments show that "de-evolutionarising" can increase performance, in some cases doubling it. Considering that the problem domain and the algorithms are arbitrarily selected from the "memetic niche", it seems likely that the same effect occurs for other problems and algorithms. Therefore, our conclusion is that after designing and building a memetic algorithm, one should perform a verification by comparing this algorithm with its "de-evolutionarised" variant.

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Available from: Bart Craenen, Sep 01, 2015
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    • "In literature, MAs are sometimes referred to as Baldwinian EAs, Lamarckian EAs, Cultural Algorithms , or Genetic Local-Search Algorithms. MAs, however, sometimes suffer from the algorithm-of-many-parts problem, also called Memetic Overkill [10]. When MAs are afflicted by Memetic Overkill it becomes difficult to identify which parts of the algorithm contribute toward finding solutions, even whether some parts hamper finding solutions. "
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    ABSTRACT: In this paper we introduce a novel clustering algorithm based on the Memetic Algorithm meta-heuristic wherein clusters are iteratively evolved using a novel single operator employ-ing a combination of heuristics. Several heuristics are de-scribed and employed for the three types of selections used in the operator. The algorithm was exhaustively tested on three benchmark problems and compared to a classical clustering algorithm (k-Medoids) using the same performance metrics. The results show that our clustering algorithm consistently provides better clustering solutions with less computational effort.
    2013 IEEE Machine Learning for Signal Processing Workshop, Southampton; 09/2013
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    • "solution. When anybody wants to apply any of the previous techniques (EA or Swarm) to CSP problems, he/she must deal with the representation and modelling of the CSP problem in the optimal way for the selected algorithm [13], [14]. "
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    ABSTRACT: Constraint Satisfaction Problems (CSP) have been widely studied in several research areas like Artificial Intelligence or Operational Research due their complexity and industrial interest. From previous research areas, heuristic (informed) search methods have been particularly active looking for feasible approaches. One of the critical problems to work with CSP is related to the exponential growth of computational resources needed to solve even the simplest problems. This paper presents a new efficient CSP graph-based representation to solve CSP by using Ant Colony Optimization (ACO) algorithms. This paper presents also a new heuristic (called Oblivion Rate), that have been designed to improve the current state-of-the-art in the application of ACO algorithms on these domains. The presented graph construction provides a strong reduction in both, the number of connections and the number of nodes needed to model the CSP. Also, the new heuristic is used to reduce the number of pheromones in the system (allowing to solve problems with an increasing complexity). This new approach has been tested, as case study, using the classical N-Queens Problem. Experimental results show how the new approach works in both, reducing the complexity of the resulting CSP graph and solving problems with increasing complexity through the utilization of the Oblivion Rate.
    Evolutionary Computation (CEC), 2013 IEEE Congress on; 01/2013
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    ABSTRACT: Tesis Univ. Granada. Departamento de Ciencias de la Computación e Inteligencia Artificial. Leída el 5 de octubre de 2007
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