The extended classifier systems (XCS) by producing a set of rules is (classifier) trying to solve learning problems as online. XCS is a rather complex combination of genetic algorithm and reinforcement learning that using genetic algorithm tries to discover the encouraging rules and value them by reinforcement learning. Among the important factors in the performance of XCS is the possibility to...

This short note presents a discussion arisen after reading ”Development a new mutation operator to solve the Traveling Salesman Problem by aid of Genetic Algorithms”, by Murat Albayrak and Novruz Allahverdi, (2011). Expert System with Applications (38)(pp. 1313-1320). The discussed paper presents a new greedy mutation operator to solve the well-known Traveling Salesman Problem. To prove the qua...

Two mechanisms have recently been proposed that can significantly speed up finding distant improving solutions via mutation, namely using a random mutation rate drawn from heavy-tailed distribution (“fast mutation”, Doerr et al. (2017) [2]) and increasing the strength based on stagnation detection mechanism (Rajabi Witt (2020) [3]). Whereas latter obtain asymptotically best probability of singl...

This paper presents a comparison in the performance analysis between a newly developed mutation operator called Scaled Truncated Pareto Mutation (STPM) and an existing mutation operator called Log Logistic Mutation (LLM). STPM is used with Laplace Crossover (LX) taken from literature to form a new generational RCGA called LX-STPM. The performance of LX-STPM is compared with an existing RCGA cal...

Differential Evolution (DE) is one of the most successful and powerful evolutionary algorithms for global optimization problem. The most important operator in this algorithm is mutation operator which parents are selected randomly to participate in it. Recently, numerous papers are tried to make this operator more intelligent by selection of parents for mutation intelligently. The intelligent s...

The mutation and cross-over operators are, with selection, the foundation of genetic algorithms. We show in this paper, some possibilities offered by these operators. Having explained the specificity of the most known operators (1-point, p-point and uniform cross-over, classical and deterministic mutation) we introduce new crossover and mutation operators with a low cost in term of execution ti...

In this paper, we have proposed three mutation operators to produce next generation in Genetic Algorithm. Our research work focuses on solving Multiple Sequence Alignment (MSA) problem by using GA with different mutation operators like gap shift, space merging, full gap column remover. To know the population evolution and quality of the sequence aligned, several studies and tests have been perf...

Inversion mutation is a typical genetic operator for combinatorial problems like the traveling salesman problem (TSP). It reverses the tour between two randomly chosen cities. Here, we propose a self-adaptive variant of inversion mutation. Self-adaptation is a successful control technique for the mutation strength. Up to now, the concept of self-adaptation has not been applied to inversion muta...