CLERGUE , Manuel

This paper presents a neurogenesis process based on the protein regulation system. The novelty consists in applying some genetic algorithm work with neurogenesis, in particular work on genetic operators and work on fitness landscapes. In order to get relevant tests with reduced simulation costs, neurogenesis is applied to a well know problem,...

Genetic algorithms are known to be convergent algorithms, with final population tending to become homogeneous. In this paper we show that it is possible to exhibit complex dynamics for a genetic algorithm by slightly modifying the canonical algorithm. Indeed, adding a meta level in the interpretation of the individuals, associated with a...

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We propose to construct hard functions for genetic algorithms by combining two types of misleading functions. We consider on one hand the traditional Trap functions defined over the unitation, and on the other hand new Trap functions based on the alternation. We recall the performance of GA on these functions as well as the results on the...

This paper deals with the way dual genetic algorithms (dga), an extension of the standard ones, explore the search space. After a brief introduction presenting genetic algorithms and dualism, the fitness distance correlation is discussed in the context of dualism. From this discussion, a conjecture is made about the genetic heuristic used by...

Initially, Artificial Evolution focuses on Evolutionary Algorithms handling solutions coded in fixed length structures. In this context, the role of crossover is clearly the mixing of information between solutions. The development of Evolutionary Algorithms operating on structures with variable length, of which genetic programming is one of the...

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Usually the offspring-parent fitness correlation is used to visualize and analyze some caracteristics of fitness landscapes such as evolvability. In this paper, we introduce a more general representation of this correlation, the Fitness Cloud (FC). We use the bottleneck metaphor to emphasise fitness levels in landscape that cause local search...

This paper proposes an optimization approach for solving a classical problem in cellular automata theory: the 6-states Firing Squad Synchronization Problem (FSSP). To this purpose, we introduce an original optimization function which quantifies the quality of solutions according only to the main goal of the problem without taking into account...

Chacun peut observer dans le monde réel l'omniprésence de systèmes complexes constitués de nombreux éléments en interaction et dont les caractéristiques globales ne peuvent se réduire à celles de leurs composants : ce sont par exemple les cellules d'un organisme vivant, les colonies de fourmis, les écosystèmes, ou encore les systèmes...

Choosing the suitable representation, the operators and the values of the parameters of an evolutionary algorithm is one of the main problems to design an efficient algorithm for one particular optimization problem. This additional information to the evolutionary algorithm generally is called the algorithm parameter, or parameter. This work...

This paper introduces the concept of fitness cloud as an alternative way to visualize and analyze search spaces than given by the geographic notion of fitness landscape. It is argued that the fitness cloud concept overcomes several deficiencies of the landscape representation. Our analysis is based on the correlation between fitness of...

We proposed a new search heuristic using the scuba diving metaphor. This approach is based on the concept of evolvability and tends to exploit neutrality which exists in many real-world problems. Despite the fact that natural evolution does not directly select for evolvability, the basic idea behind the scuba search heuristic is to explicitly...

We proposed a new search heuristic using the scuba diving metaphor. This approach is based on the concept of evolvability and tends to exploit neutrality in fitness landscape. Despite the fact that natural evolution does not directly select for evolvability, the basic idea behind the scuba search heuristic is to explicitly push the evolvability...

This theoretical work defines the measure of autocorrelation of evolvability in the context of neutral fitness landscape. This measure has been studied on the classical MAX-SAT problem. This work highlight a new characteristic of neutral fitness landscapes which allows to design new adapted metaheuristic.

Evolutionary algorithms (EA) are optimization algorithms inspired by the neo-dar winian theory of evolution. Those algorithms use a population of potential solutions which is gradually guided towards better solutions discovered by random variation. The neutral theory of evolution considers that the majority of mutations are selectively neutral...

A lot of recent research papers focus on the challenging problem of the combination of genetic algorithms and artificial neural networks. Developmental and molecular biology may be a source of inspiration for designing powerful artificial neurogenesis systems allowing the generation of complex modular structures.

Previous works have shown the efficiency of a new approach for the Genetic Algorithms, the Dual Genetic Algorithms, in the multiobjective optimization context. Dual Genetic Algorithms make use of a meta level to enhance the expressiveness of schemata, entities implicitly handle by Genetic Algorithms. In this paper, we show that this approach,...

he paper proposes the design of difficult functions for a GA (genetic algorithm) where the deceptive attractor is at mid-distance from the global optimum. First, piecewise-linear trap functions of alternation are investigated. We consider alternation based distance to enable the ability of fitness distance correlation coefficient to predict GA...

Most of the Evolutionary Algorithms handling variable-sized structures, like Genetic Programming, tend to produce too long solutions and the recombination operator used is often considered to be partly responsible of this phenomenon, called bloat. The Maximum Homologous Crossover (MHC) preserves similar structures from parents by aligning them...

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We introduce a new recombination operator, the Maximum Homologous Crossover for Linear Genetic Programming. In contrast to standard crossover, it attempts to preserve similar structures from parents, by aligning them according to their homology, thanks to an algorithm used in Bio-Informatics. To highlight disruptive effects of crossover...

Our aim in this paper is to analyse the phenotypic effects (evolvability) of diverse coding conversion operators in an instance of the states based evolutionary algorithm (SEA). Since the representation of solutions or the selection of the best encoding during the optimization process has been proved to be very important for the efficiency of...

In this paper we introduce a new selection scheme in cellular genetic algorithms (cGAs). Anisotropic Selection (AS) promotes diversity and allows accurate control of the selective pressure. First we compare this new scheme with the classical rectangular grid shapes solution according to the selective pressure: we can obtain the same takeover...