Carlos M. Fonseca

Learn More
The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to allow direct intervention of an external decision maker(More)
An important issue in multiobjective optimization is the quantitative comparison of the performance of different algorithms. In the case of multiobjective evolutionary algorithms, the outcome is usually an approximation of the Pareto-optimal front, which is denoted as an approximation set, and therefore the question arises of how to evaluate the quality of(More)
The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial performance measures with the inherently scalar way in which EAs(More)
In optimization, multiple objectives and constraints cannot be handled independently of the underlying optimizer. Requirements such as continuity and di erentiability of the cost surface add yet another con icting element to the decision process. While \better" solutions should be rated higher than \worse" ones, the resulting cost landscape must also comply(More)
This work proposes a quantitative, non-parametric interpretation of statistical performance of stochastic multiobjective optimizers, including, but not limited to, genetic algorithms. It is shown that, according to this interpretation, typical performance can be defined in terms analogous to the notion of median for ordinal data, as can other measures(More)
The performance of stochastic optimisers can be assessed experimentally on given problems by performing multiple optimisation runs, and analysing the results. Since an optimiser may be viewed as an estimator for the (Pareto) minimum of a (vector) function, stochastic optimiser performance is discussed in the light of the criteria applicable to more usual(More)
Acknowledgements The production of this Toolbox was made possible by a UK SERC grant on " Genetic Algorithms in Control Systems Engineering " (GR/J17920). Many thanks are due to Hartmut Pohlheim, a visiting researcher from the Technical University Ilmenau, Germany, for the support for real-valued genetic algorithms and his hard work in coding and revising(More)
In this talk, fitness assignment in multiobjective evolutionary algorithms is interpreted as a multi-criterion decision process. A suitable decision making framework based on goals and priorities is formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies, including constraint satisfaction,(More)
The goal of multi-objective optimization is to find a set of best compromise solutions for typically conflicting objectives. Due to the complex nature of most real-life problems, only an approximation to such an optimal set can be obtained within reasonable (computing) time. To compare such approximations, and thereby the performance of multi-objective(More)