In this paper, a novel search operation is proposed for the neuroevolution of augmented topologies, namely difference-based mutation. This operator uses differences between individuals in population to perform more efficient optimal weights and structure model. The difference determined according innovation numbers assigned each node connection, allowing tracking changes. implemented algorithm ...

This paper proposes an effective mutation operator for Cooperative Genetic Algorithm (CGA) to be applied to a practical Nurse Scheduling Problem (NSP). NSP is a complex combinatorial optimizing problem for which many requirements must be considered. The changes of the shift schedule yields various problems, for example, a drop in the nursing level. The author describes a technique of the reopti...

Harmony Search (HS) is a recently developed stochastic algorithm which imitates the music improvisation process. In this process, the musicians improvise their instrument pitches searching for the perfect state of harmony. Practical experiences, however, suggest that the algorithm suffers from the problems of slow and/or premature convergence over multimodal and rough fitness landscapes. This p...

Differential evolution algorithm has been widely used, because of its efficient optimization and no complex operation and coding mechanism. But DE falls into the local optimum easily. So this study puts forward a memetic algorithm. The algorithm can increase the diversity of population and jump out the local extreme value point effectively. The convergence speed of the algorithm is improved, be...

Metaoptimization is a way of tuning parameters of an optimization algorithm with use of a higher-level optimizer. In this paper it is applied to the problem of choosing among possible mutation range adaptation schemes in Differential Evolution (DE). We consider a new version of DE, called DE/rand/∞. In this algorithm, differential mutation is replaced by a Gaussian one, where the covariance mat...

The goal of this paper is to study the dynamics of a genetic algorithm in a more visual, intuitive way than statistical methods do. Rather than modeling the genetic algorithm mathematically, we try to describe how the GA scans through the search space, and how genotypes move through the search space as genetic operators are applied to them. We find that standard genetic operators can be describ...

In the GA approach the parameters that influence its performance include population size, crossover rate and mutation rate. Genetic algorithms are suitable for traversing large search spaces since they can do this relatively fast and because the mutation operator diverts the method away from local optima, which will tend to become more common as the search space increases in size. GA’s are base...

Abstract The problem of finding roots equations has always been an important research in the fields scientific and engineering calculations. For standard differential evolution algorithm cannot balance convergence speed accuracy solution, improved is proposed. First, one-half rule introduced mutation process, that is, half individuals perform evolutionary mutation, other strategy reorganization...

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and...

Genetic Algorithms (GAs) have proven to be a useful means of finding optimal or near optimal solutions to hard problems that are difficult to solve by other means. However, determining which crossover and mutation operator is best to use for a specific problem can be a complex task requiring much trial and error. Furthermore, different operators may be better suited to exploring the search spac...