Researchers have been applying artificial/computational intelligence (AI/CI) methods to computer games. In this research field, further researchesare required to compare AI/CI methods with respect to each game application. In thispaper, we report our experimental result on the comparison of evolution strategy, genetic algorithm and their hybrids, applied to evolving controller agents for MarioA...

In this paper, an efficient sine cosine differential gradient-based optimization method is proposed for identifying unknown parameters of photovoltaic models. the simulation, parameter identification formulated as objective function to be minimized based on error between estimated and experimental data. Based original method, combines mutation crossover evolution algorithm. Specifically, operat...

We propose a new genetic algorithm with optimal recombination for the asymmetric instances of travelling salesman problem. The algorithm incorporates several new features that contribute to its effectiveness: (i) Optimal recombination problem is solved within crossover operator. (ii) A new mutation operator performs a random jump within 3-opt or 4-opt neighborhood. (iii) Greedy constructive heu...

Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness these EAs comes from components and parameters. These solution representation, selection, crossover, mutation. Unfortunately, almost all EA based complex de...

This paper develops a new crossover operator, Sequential Constructive crossover (SCX), for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The sequential constructive crossover operator constructs an offspring from a pair of parents using better edges on the basis of their values that may be present in the parents' structure maintaining the seq...

In a canonical genetic algorithm, the reproduction operators (crossover and mutation) are random in nature. The direction of the search carried out by the GA system is driven purely by the bias to fitter individuals in the selection process. Several authors have proposed the use of directed mutation operators as a means of improving the convergence speed of GAs on problems involving real-valued...

The present study is an attempt to propose a mutation-based real-coded genetic algorithm (MBRCGA) for sizing and layout optimization of planar and spatial truss structures. The Gaussian mutation operator is used to create the reproduction operators. An adaptive tournament selection mechanism in combination with adaptive Gaussian mutation operators are proposed to achieve an effective search in ...

Gene expression programming (GEP) uses mutation, transposition , and crossover to create variation. Although there exists a large body of work in genetic algorithms concerning the roles of mutation and recombination, these results not only do not apply to GEP due to the genotype/ phenotype representation but also seem to contradict the GEP experience. Therefore, and given the diversity of GEP o...

In this paper novel hybrid genetic algorithms for Open--Shop Scheduling Problem (OSSP) are presented. Two greedy heuristics LPT-Task and LPT-Machine are proposed for decoding chromosomes represented by permutations with repetitions. For comparison the standard permutation representation of OSSP instances is used. The algorithms apply also efficient crossover operator LOX and mutation operators ...