The research into the ability of building self-learning natural language parser based on context–free grammar (CFG ) was presented. The paper investigates the use of evolutionary methods: a genetic algorithm, a genetic programming and learning classifier systems for inferring CFG based parser. The experiments were conducted on the real set of natural language sentences. The gained results confi...

This paper presents an approach for portfolio selection using evolutionary programming as a tool for optimization. The goal is to find the mix of stocks that minimize risk expressed as standard deviation for a certain expected return. Two alternatives approaches are developed (Hillclimbing and Random) to measure the performance of the modified genetic algorithm. Key-Words: Evolutionary Computin...

In this paper, we describe a novel approach to image quantisation by using a genetic algorithm to find near-optimal code words. Moving away from the traditional binary string representations of genetic data, the proposed system applies genetic operators directly on pixel blocks thus avoiding any conversion to a conventional format. To operate on the data this way, operators for fitness, combina...

In this paper, we describe a novel approach to image quantisation by using a genetic algorithm to find near-optimal code words. Moving away from the traditional binary string representations of genetic data, the proposed system applies genetic operators directly on pixel blocks thus avoiding any conversion to a conventional format. To operate on the data this way, operators for fitness, combina...

A novel design of a dual-frequency unequal power divider is presented. The design is optimized using a genetic algorithm based method in order to satisfy several requirements at two given frequencies. The method considers realistic conditions, since it assumes lossy transmission lines and unmatched real or frequency-dependent complex loads. The method can be applied in a wide range of applicati...

This paper tests two heuristic approaches by applying them to the magnetic hysteresis model parameter identification. Both Preisach and Jiles-Atherton hysteresis models have been used. The implemented investigated heuristics are genetic algorithm and simulated annealing. Both the heuristic algorithms efforts the knowledge of the B-H experimental points of the hysteresis loop to be modeled accor...

This paper describes a new procedure to design robust H∞ controller with time domain specifications and a fault detection method for MIMO system. Controller design parameters and fault detection method are calculated solving multi-objective optimization problems which are based on genetic algorithms. Simulations tests are carried out to evaluate our design procedure, and satisfactory simulation...

An optimal experimental design for yield coefficients estimation in an unstructured growth model of fed-batch fermentation of E. coli is presented. The feed profile is designed by optimisation of a scalar function based on the Fischer Information Matrix. A genetic algorithm is proposed as the optimisation method due to its efficiency and independence on the initial values. Copyright 2004 IFAC K...