Feature selection is one of the most pervasive problems in pattern recognition. It can be posed as a multi-objective optimisation problem, since, in the simplest case, it involves feature subset cardinality minimisation and performance maximisation. In many problem domains, such as in medical or engineering diagnosis, performance can more appropriately be… (More)
Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective… (More)
The optimal matching of a small gas turbine in the frame of a hybrid SOFC-GT is examined in the present paper. The behaviour of the gas turbine is examined for the design point and part-load operation regimes. Special emphasis is placed on the design requirements placed on the compressor and turbine for optimal operation of the hybrid system. At the same… (More)
A hybrid local-global and deterministic-evolutionary strategy is proposed for the reduction of objective function calls when Pareto Optimal front approximation is considered in multiobjective optimization problems arising from electromagnetic shape design. Both analytical and real-life test cases are discussed stressing the key-point of switching criteria.
In this paper, we propose a new type of parallel genetic algorithm model for multi objective optimization problems. That is called a " Master-Slave model with Local Cultivation model (MSLC) ". To clarify the characteristics and effectiveness of this model, the proposed model and the various EAs are applied to solve an antenna arrangement problem of mobile… (More)
This paper examines implementation models for distributed memory architectures of a Parallel Simulated Annealing using Genetic Crossover (PSA/GAc). The PSA/GAc that was proposed by authors is the algorithm, where there are several processes of a simulated annealing working parallel. To exchange information between the solutions, the operation of genetic… (More)
This paper describes techniques that can be applied to large-scale real-life employee timetabling problems. The problems are characterized as constraint satisfaction problems. The solution methodology uses genetic algorithms to minimize the total penalty for constraint violation. An encoding for search space reduction, genetic operation for a quick… (More)
The CPEA, an Evolutionary Algorithm that preserves diversity by finding clusters in the population, has had its convergence performance improved by a technique tentatively called 'evolutionary operator selection'. Performance is compared to results found in the literature, though at the moment it is not entirely clear how the evolutionary operator selection… (More)