In this paper, we propose a genetic algorithm, called GenSPN, for finding highly probable differential characteristics of substitution permutation networks (SPNs). A special fitness function and a heuristic mutation operator have been used to improve the overall performance of the algorithm. We report our results of applying GenSPN for finding highly probable differential characteristics of Ser...

The success of artificial neural network evolution is determined by many factors. One of these factors is the fitness function used in genetic algorithm. Fitness function determines selection pressure and Therefore influences the direction of evolution. It decides, whether received artificial neural network will be able to fulfill its tasks. Three fitness functions are proposed and examined in ...

An evolutionary algorithm program has been used to investigate the evolution of the universal and several alternate genetic codes in biology. Various fitness functions were evaluated using a fixed set of genes and a genetic code that mutates every generation for up to 1000 organisms. The evolution of the population was found to be stable if starting with the universal genetic code.

Surrogate models of fitness have been presented as a way of reducing the number of fitness evaluations required by evolutionary algorithms. This is of particular interest with expensive fitness functions where the time taken for building the model is outweighed by the saving of using fewer function evaluations. In this paper, we show how a Markov network model can be used as a surrogate fitness...

To reproduce an image, it is necessary to map out of gamut colors of the image to destination gamut. It is clear that the best color gamut mapping introduces the perceptually closest image to the original one. In this study, a new color gamut mapping is purposed by the aid of Genetic Algorithm (GA). The color difference between the original and mapped images based on S-LAB formula was chosen as...

Deceptivity and epistasis both contribute to make fitness functions hard to optimize for a genetic algorithm. In this note we examine the relation between these concepts, with particular emphasis on their mutual reinforcement.

A new hybrid algorithm of Particle Swarm Optimization and Genetic Algorithm (PSOGA) is presented to get the optimum design of truss structures with discrete design variables. The objective function chosen in this paper is the total weight of the truss structure, which depends on upper and lower bounds in the form of stress and displacement limits. The Particle Swarm Optimization basically model...

Multi-objective evolutionary algorithms using the weighted sum of the objectives as the fitness functions feature simple execution and effectiveness in multiobjective optimization. However, they cannot find the Pareto solutions on the non-convex part of the Pareto frontier, and thus are difficult to find evenly distributed solutions. Under the circumstances, this paper proposes a new evolutiona...