Hugo Alfonso

Enhanced evolutionary algorithms for single and multiobjective optimization in the job shop scheduling problem

Abstract Over the past few years, a continually increasing number of research efforts have investigated the application of evolutionary computation techniques for the solution of scheduling problems. Scheduling can pose extremely complex combinatorial optimization problems, which belong to the NP-hard family. Last enhancements on evolutionary algorithms include new multirecombinative approaches. Multiple Crossovers Per Couple (MCPC) allows multiple crossovers on the couple selected for mating and Multiple Crossovers on Multiple Parents (MCMP) do this but on a set of more than two parents. Techniques for preventing incest also help to avoid premature convergence. Issues on representation and operators influence efficiency and efficacy of the algorithm. The present paper shows how enhanced evolutionary approaches, can solve the Job Shop Scheduling Problem (JSSP) in single and multiobjective optimization.

Advanced models of cellular genetic algorithms evaluated on SAT

Cellular genetic algorithms (cGAs) are mainly characterized by their spatially decentralized population, in which individuals can only interact with their neighbors. In this work, we study the behavior of a large number of different cGAs when solving the well-known 3-SAT problem. These cellular algorithms differ in the policy of individuals update and the population shape, since these two features affect the balance between exploration and exploitation of the algorithm. We study in this work both synchronous and asynchronous cGAs, having static and dynamically adaptive shapes for the population. Our main conclusion is that the proposed adaptive cGAs outperform other more traditional genetic algorithms for a well known benchmark of 3-SAT.

Variable size population in parallel evolutionary algorithms

Considering the population size is a critical parameter to define in evolutionary computation, in this paper an improved parallel evolutionary algorithm that incorporates different mechanisms to adapt the population size to the current status, is presented. Those mechanisms are based on resizing on fitness improvement GA (PRoFIGA) and variable population size (GAVaPS). Results indicate these incorporations are a reasonable choice when refinement in solutions is necessary.

Comparison of Recombination Operators in Panmictic and Cellular GAs to Solve a Vehicle Routing Problem

The Vehicle Routing Problem (VRP) deals with the assignment of a set of transportation orders to a fleet of vehicles, and the sequencing of stops for each vehicle to minimize transportation costs. In this paper we study the Capacitated VRP (CVRP), which is mainly characterized by using vehicles of the same capacity. Taking a basic GA to solve the CVRP, we propose a new problem dependent recombination operator, called Best Route Better Adjustment recombination (BRBAX). A comparison of its performance is carried out with respect to other two classical recombination operators. Also we conduct a study of different mutations in order to determine the best combination of genetic operators for this problem. The results show that the use of our specialized BRBAX recombination outperforms the others more generic on all problem instances used in this work for all the metrics tested.

Constrained two-dimensional non-guillotine cutting problem an evolutionary approach

General cutting problems are concerned with finding the best allocation of a number of items in larger containing regions. These problems can be encountered in numerous areas such as computer science, industrial engineering, logistics, manufacturing, among others. They belong to the family of NP-complete problems. For cases of high complexity deterministic and exact techniques become inefficient due to the vast number of possible solutions that have to be evaluated. In order to reduce the computational load, heuristic or meta-heuristic algorithms are used. The solution method presented in this paper is meta-heuristic based on an evolutionary approach, being its goal to maximize the total value of cut pieces. For that, a modification of Beasleys representation is adopted and for evaluating solutions three placement heuristic rules are developed. Moreover, the effect that placement rules has on evolutionary algorithms performance is tested. Computational results are presented for a number of test problems taken from the literature. The results are very encouraging.