This paper presents a comparison in the performance analysis between a newly developed crossover operator called Rayleigh Crossover (RX) and an existing crossover operator called Laplace Crossover (LX). Coherent to the previously defined Scaled Truncated Pareto Mutation (STPM) operator to form two (2) generational RCGAs called RX-STPM and LX-STPM, both crossovers are utilized. A set of ten (10)...

Most real-world optimization problems consist of linear cost functions subject to a set of constraints. In genetic algorithms the techniques for coping with such constraints are manifold: penalty functions, keeping the population in the feasible region, etc. Mutation and crossover operators must take into account the specific features of this kind of problems, as they are the responsible of the...

Crossover operators that preserve common components can also preserve representation level constraints. Consequently, these constraints can be used to beneficially reduce the search space. For example, in flow shop scheduling problems with order-based objectives (e.g. tardiness costs and earliness costs), search space reductions have been implemented with precedence constraints. Experiments sho...

The choice of genetic operators is one way in which genetic algorithms can be tailored to speciic optimization problems. For bit represented problems, the choice of crossover operator , or the choice not to use a crossover operator, can dramatically aaect search performance. The eecacy of crossover for genetic search is governed by the relationship between the crossover biases and the search pr...

This paper focuses on using the tness landscape paradigm in order to gain a better understanding of the behavior of local search heuristics in order to solve NP-hard problems. We concentrate ourselves on the symmetric planar TSP and we compare experimental facts about landscapes associated with the well-known 2-change and city-swap operators. We measure the \\tness-distance correlation" (FDC), ...

Genetic algorithms (GAs) are multi-dimensional, blind & heuristic search methods which involves complex interactions among parameters (such as population size, number of generations, various type of GA operators, operator probabilities, representation of decision variables etc.). Our belief is that GA is robust with respect to design changes. The question is whether the results obtained by GA d...

Genetic algorithms (GAs) are multi-dimensional, blind & heuristic search methods which involves complex interactions among parameters (such as population size, number of generations, various type of GA operators, operator probabilities, representation of decision variables etc.). Our belief is that GA is robust with respect to design changes. The question is whether the results obtained by GA d...

Genetic algorithms (GAs) are multi-dimensional, blind heuristic search methods that involve complex interactions among parameters (such as population size, number of generations, GA operators and operator probabilities). The question whether the quality of results obtained by GAs depend upon the values given to these parameters, is a matter of research interest. This work studies the problem of...

In recent years, the genetic programming crossover operator has been criticized on both theoretical and empirical grounds. This paper introduces a new crossover operator for linear genomes that encourages the emergence of positional homology in the population. Preliminary experimental results suggest that this approach is a promising direction for redesign of the mechanism of crossover.

Since crossover operator plays a vital role in genetic algorithms (GAs), several crossover operators have been proposed for the travelling salesman problem, which are then modified for the quadratic assignment problem (QAP). In this paper, we modify the sequential constructive crossover (SCX) operator for a simple GA to find heuristic solution to the QAP. Efficiency of the proposed GA using SCX...