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Traditionally in Genetic Algorithms, the mutation probability parameter maintains a constant value during the search. However, an important difficulty is to determine a priori which probability value is the best suited f...

In this preliminary study the Flow Shop Scheduling Problem (FSSP) is solved by hybrid Evolutionary Algorithms. The algorithms are obtained as a combination of an evolutionary algorithm, which uses the Multi-Inver-Over op...

Selection mechanisms favour reproduction of better individuals imposing a direction on the search process. According to this it is expected that the effective number of offspring of an individual in the next generation w...

In the restricted single-machine common due date problem, the goal is to find a schedule for the n jobs which jointly minimizes the sum of earliness and tardiness penalties. This problem, even in its simplest formulatio...

In a production system it is usual to stress minimum tardiness to achieve higher client satisfaction. According to the client relevance, job processing costs and requirements, and various other considerations, a weight ...

Classi cation and regression ensembles sho w generalization capabilities that outperform those of single predictors. We present here a further ev aluation of tw o algorithms for ensemble construction recently proposed by...

This work is focussed on the development and analysis of a new class of algorithms, called cellular memetic algorithms (cMAs), which will be evaluated here on the satisfiability problem (SAT). For describing a cMA, we st...

In this work, we study the behavior of several cellular memetic algorithms (cMAs) when solving the satisfiability problem (SAT). The proposed cMAs are the result of including hybridization techniques in dif- ferent struc...

Document classification has already been widely studied. In fact, some studies compared feature selection techniques or feature space transformation whereas some others compared the performance of different algorithms. R...

In genetic algorithms selection mechanisms aim to favour reproduction of better individuals imposing a direction on the search process. It does not create new individuals; instead it selects comparatively good individual...

To find a good termination criterion for genetic algorithms is a difficult and frequently ignored task. In most instances the practitioner stops the algorittm after a predefined number of generations or function evaluati...

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most ...

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most ...

La información extraída de especimenes biológicos es inherentemente tridimensional. Los datos tridimensionales (3D) permiten un mejor entendimiento de las estructuras y los eventos biológicos, comparados con sus proy...

The study of earliness and tardiness penalties in scheduling is a relatively recent area of research. In the past, traditionally the emphasis was put on regular measures that are nondecreasing in job completion times suc...

Evolutionary algorithms (EAs) offer a robust approach to problem solving. EAs are extremely flexible and can be extended by incorporating alternative approaches to favour he search process. One way is to hybridize an evo...

Even if simply stated the travelling salesman problem (TSP) is one of the most studied NP-hard problems. Many algorithms have been proposed to solve TSP. Dynamic programming and branch and bound techniques provided the g...

The choice of a search algorithm can play a vital role in the success of a scheduling application. Evolutionary algorithms (EAs) can be used to solve this kind of combinatorial optimization problems. Compared to conventi...

The travelling salesman problem (TSP) is a NP-hard problem. Techniques as either Branch and Bound or Dynamic Programming supplied the global optimum solution for instances with more than 7000 cities. But, ther needed mor...

Provision of population diversity is one of the main goals to avoid premature convergence in Evolutionary Algorithms (EAs). In this way the risk of being trapped in local optima is minimised. Eshelman and Shaffer [4] att...

Evolutionary algorithms (EAs) have been successfully applied to scheduling problems. Current improvements towards convergence issues in EAs include incest prevention and multiplicity features. A multiplicity feature all...

A new issue for combinatorial optimization problems is to incorporate local search into the framework of evolutionary algorithms, leading to hybrid evolutionary algorithms. With the hybrid approach, evolutionary algorith...

Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian strive for survival. They can be used to find approximate solutions to n...

Contrasting with conventional approaches to crossover, Multiple Crossover Per Couple (MCPC) is an alternative, recently proposed [1], approach under which more than one crossover operation for each mating pair is allowed...