s: This paper presents a novel approach to improve the performance of genetic algorithms called genetic algorithms with real-coded binary representation (GARB). The proposed algorithm is capable of maintaining the population diversity during the whole run which protects it from premature convergence. This is achieved by using a special encoding scheme with a high redundancy, which is supported ...

The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adap-tive genetic algorithms have been built for inducing suitable exploitation/exploration relationships for avoiding the premature convergence problem. Some adaptive genetic algorithms are built using fuzzy logic techniques. In this paper, we summarize two types of such appr...

An experiment is described with demonstrates that certain highly specific disruptive effects in immediate memory for tonal pitch generalize across octaves. Pitch recognition was required after a retention interval during which six other tones were played. The effects of including in the interpolated sequence tones which were removed by exactly an octave from those which had already been demonst...

In this work we develop and compare multi-parent crossover operators based on the extraction of characteristics from the best individuals in the population (average, median, standard deviation and quantiles). These statistics evolve in parallel with the algorithm. The proposed operators are used in combination with a real-coded genetic algorithm for the evolution of polynomial functions to solv...

This paper presents a real-coded memetic algorithm that combines a high diversity global exploration with an adaptive local search method to the most promising individuals that adjusts the local search probability and the local search depth. In our proposal we use the individual fitness to decide when local search will be applied (local search probability) and how many effort should be applied ...

today, with the advanced statistical techniques and neural networks, predictive models of distribution have been rapidly developed in ecology. purpose of this research is to predict and map the distribution of tetranychus urticae koch (acari: tetranychidae) using mlp neural networks combined with genetic algorithm in surface of farm. population data of pest was obtained in 2016 by sampling in 1...

In recent decades, considerable research efforts have been devoted to using machine learning or data mining techniques to automatically discover the parameters in multi-objective functions. Among these techniques, the genetic algorithms have been recognized to be particularly powerful in multi-objective problems. Genetic algorithms play a significant role, as search techniques for handling comp...