Scuba Search : when selection meets innovation

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 to increase. The search process switches between two phases: Conquest-of-the-Waters and Invasion-of-the-Land. A comparative study of the new algorithm and standard local search heuristics on the NKq-landscapes has shown advantage and limit of the scuba search. To enlighten qualitative differences between neutral search processes, the space is changed into a connected graph to visualize the pathways that the search is likely to follow

Where are Bottlenecks in NK Fitness Landscapes?

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 process to slow down. For a local search heuristic such as hill-climbing or simulated annealing, FC allows to visualize bottleneck and neutrality of landscapes. To confirm the relevance of the FC representation we show where the bottlenecks are in the well-know NK fitness landscape and also how to use neutrality information from the FC to combine some neutral operator with local search heuristic

Measuring the Evolvability Landscape to study Neutrality

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

Anisotropic selection in cellular genetic algorithms

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 time with the two techniques although the spreading of the best individual is different. We then give experimental results that show to what extent AS promotes the emergence of niches that support low coupling and high cohesion. Finally, using a cGA with anisotropic selection on a Quadratic Assignment Problem we show the existence of an anisotropic optimal value for which the best average performance is observed. Further work will focus on the selective pressure self-adjustment ability provided by this new selection scheme

States based evolutionary algorithm

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 introduces a new evolutionary algorithm, States based Evolutionary Algorithm which is able to combine different evolutionary algorithms with different parameters included different representations in order to control the parameters and to take the advantage of each possible evolution algorithm during the optimization process. This paper gives first experimental arguments of the efficiency of the States based EA

On the Influence of Selection Operators on Performances in Cellular Genetic Algorithms

In this paper, we study the influence of the selective pressure on the performance of cellular genetic algorithms. Cellular genetic algorithms are genetic algorithms where the population is embedded on a toroidal grid. This structure makes the propagation of the best so far individual slow down, and allows to keep in the population potentially good solutions. We present two selective pressure reducing strategies in order to slow down even more the best solution propagation. We experiment these strategies on a hard optimization problem, the quadratic assignment problem, and we show that there is a value for of the control parameter for both which gives the best performance. This optimal value does not find explanation on only the selective pressure, measured either by take over time and diversity evolution. This study makes us conclude that we need other tools than the sole selective pressure measures to explain the performances of cellular genetic algorithms