A sorting algorithm defines a path in the search space of n! permutations based on the information provided by a comparison predicate. Our generic mutation operator for hybridization, blends a hill-climber and follows the path traced by any sorting algorithm. Our proposal adds search (exploitation) capability to the mutation operator. Mutation requests swaps to construct and test new permutatio...

The purpose of this paper is to discuss the implementation and performance of two genetic operators specifically tuned to solve the Travelling Salesman Problem. The two operators discussed are the Greedy Knot-Cracker crossover operator and a modified version of the 3-opt mutation operator. Specifically the paper shall discuss the motivation for choosing these operators, their implementation, th...

In this paper, we present a study of the use of an artificial immune system (CLONALG) for solving constrained global optimization problems. As part of this study, we evaluate the performance of the algorithm both with binary encoding and with real-numbers encoding. Additionally, we also evaluate the impact of the mutation operator in the performance of the approach by comparing Cauchy and Gauss...

A generalized net model of the mutation operator for the genetic algorithm is developed. The apparatus of generalized nets is considered as an appropriate tool for describing the performance of the genetic algorithm. The proposed generalized net model is a realization of the mutation operator for the Breeder genetic algorithm. The resulting GN model can be considered as a separate net, but also...

Artificial Bee Colony (ABC) is a swarm-based metaheuristic for continuous optimization. Recent work hybridized this algorithm with other metaheuristics in order to improve performance. The work in this paper, experimentally evaluates the use of different mutation operators with the ABC algorithm. The introduced operator is activated according to a determined probability called mutation rate (MR...

In this paper, a genetic algorithm (GA) based principal component selection approach is proposed for production performance estimation in mineral processing. The approach combines a modified GA with principal component analysis (PCA) in order to improve the estimation accuracy of production performance. In this context, the extended chromosome encoding, the fitness function formed by combining ...

An important aspect of heterogeneous computing systems is the problem of efficiently mapping tasks to processors. There are various methods of obtaining acceptable solutions to this problem but the genetic algorithm is considered to be among the best heuristics for assigning independent tasks to processors. This paper focuses on how the genetic heuristic can be improved by determining the best ...

Evolutionary algorithms (EAs) have been applied to many optimization problems successfully in recent years. The genetic algorithm (GAs) and evolutionary programming (EP) are two di!erent types of EAs. GAs use crossover as the primary search operator and mutation as a background operator, while EP uses mutation as the primary search operator and does not employ any crossover. This paper proposes...

A novel ant colony system which employs a candidate set strategy based on Delaunay triangulation (CSDT) and a self-adaptive mutation operator (SMO) for TSP (DSMACS) is proposed. Under the condition that all the edges in the global optimal tour are nearly all contained in the candidate sets, CSDT can limit the selection scope of ants at each step to average six cities below, and thus substantial...

This work is a first step toward the design of a cooperative-coevolution GP for symbolic regression, which first output is a selective mutation operator for classical GP. Cooperative co-evolution techniques rely on the imitation of cooperative capabilities of natural populations and have been successfully applied in various domains to solve very complex optimization problems. It has been proved...