In this paper, multi-objective optimization is applied to determine the parameters for a k-nearest neighbours classifier that has been used in the diagnosis of Paroxysmal Atrial Fibrillation (PAF), in order to get optimal combinations of classification rate, sensibility and specificity. We have considered three different evolutionary algorithms for implementing the multiobjective optimization o...

In this study a class of Multi-Objective Genetic Algorithms (MOGAs) is proposed to select the most relevant features for the problem of speech-based emotion recognition. The employed evolutionary algorithms are the Strength Pareto Evolutionary Algorithm (or SPEA), the Preference-Inspired CoEvolutionary Algorithm with goal vectors (or PICEA), and the Nondominated Sorting Genetic Algorithm II (or...

Evolutionary Algorithms have been applied to single and multiple objectives optimization problems, with a strong emphasis on problems, solved through numerical simulations. However in several engineering problems, there is limited availability of suitable models and there is need for optimization of realistic or experimental configurations. The multiobjective optimization of an experimental set...

This paper presents a layered encoding cascade evolutionary approach to solve a 0/1 knapsack optimization problem. A layered encoding structure is proposed and developed based on the schema theorem and the concepts of cascade correlation and multi-population evolutionary algorithms. Genetic algorithm (GA) and particle swarm optimization (PSO) are combined with the proposed layered encoding stru...

This paper presents an innovative active power filter design method to simultaneously compensate the current harmonics and reactive power of a nonlinear load. The power filter integrates a passive power filter which is a RL low-pass filter placed in series with the load, and an active power filter which comprises an RL in series with an IGBT based voltage source converter. The filter is assumed...

In a recent paper by Tang, Reed and Wagener (2006, hereafter referred to as TRW) a comparison assessment was presented of three state-of-the-art evolutionary algorithms for multiobjective calibration of hydrologic models. Through three illustrative case studies, TRW demonstrate that the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Epsilon Dominance Nondominated Sorted Genetic Algorithm ...

The paper describes further developments of the interactive evolutionary design concept relating to the emergence of mutually inclusive regions of high performance design solutions. These solutions are generated from cluster-oriented genetic algorithm (COGAs) output and relate to a number of objectives introduced during the preliminary design of military airframes. The datamining of multi-objec...

This paper proposes a new multi-objective genetic algorithm, called GAME, to solve constrained optimization problems. GAME uses an elitist archive, but it ranks the population in several Pareto fronts. Then, three types of fitness assignment methods are defined: the fitness of individuals depends on the front they belong to. The crowding distance is also used to preserve diversity. Selection is...

A new hybrid multi-objective, multivariable optimizer utilizing Strength Pareto Evolutionary Algorithm (SPEA), Non-dominated Sorting Differential Evolution (NSDE), and Multi-Objective Particle Swarm (MOPSO) has been created and tested. The optimizer features automatic switching among these algorithms to expedite the convergence of the optimal Pareto front in the objective function(s) space. The...

A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default pa...