A solution to the crucial problem of population degeneration in high-dimensional evolutionary optimization

Three popular evolutionary optimization algorithms are tested on high-dimensional benchmark functions. An important phenomenon responsible for many failures - population degeneration - is discovered. That is, through evolution, the population of searching particles degenerates into a subspace of the search space, and the global optimum is exclusive from the subspace. Subsequently, the search will tend to be confined to this subspace and eventually miss the global optimum. Principal components analysis (PCA) is introduced to discover population degeneration and to remedy its adverse effects. The experiment results reveal that an algorithm's efficacy and efficiency are closely related to the population degeneration phenomenon. Guidelines for improving evolutionary algorithms for high-dimensional global optimization are addressed. An application to highly nonlinear hydrological models demonstrates the efficacy of improved evolutionary algorithms in solving complex practical problems. © 2011 IEEE

A computational model of evolution: haploidy versus diploidy

In this paper, the study of diploidy is introduced like and important mechanism for memory reinforcement in artificial environments where adaptation is very important. The individuals of this ecosystem are able to genetically "learn" the best behaviour for survival. Critical changes, happening in the environmental conditions, require the presence of diploidy to ensure the survival of species. By means of new gene-dominance configurations, a way to shield the individuals from erroneous selection is provided. These two concepts appear like important elements for artificial systems which have to evolve in environments with some degree of instability.Publicad

Genetic programming for the automatic design of controllers for a surface ship

In this paper, the implementation of genetic programming (GP) to design a contoller structure is assessed. GP is used to evolve control strategies that, given the current and desired state of the propulsion and heading dynamics of a supply ship as inputs, generate the command forces required to maneuver the ship. The controllers created using GP are evaluated through computer simulations and real maneuverability tests in a laboratory water basin facility. The robustness of each controller is analyzed through the simulation of environmental disturbances. In addition, GP runs in the presence of disturbances are carried out so that the different controllers obtained can be compared. The particular vessel used in this paper is a scale model of a supply ship called CyberShip II. The results obtained illustrate the benefits of using GP for the automatic design of propulsion and navigation controllers for surface ships

The optimal resource allocation in stochastic activity networks via the evolutionary approach : a platform implementation in Java

An optimal resource allocation approach to stochastic multimodal projects had been previously developed by applying a Dynamic Programming Model, which proved to be very demanding computationally. A new approach, the Electromagnetism Algorithm had also been adapted and implemented, with better results than the Dynamic Programming Model. This paper presents another philosophy for solving the same problem, based on an Evolutionary Algorithm. This approach was implemented using an Object Oriented language, Java, and its results were compared to the Electromagnetism Algorithm. A distributed version was also developed, to be run in a computer network, in order to take advantage of available computational resources.Fundação para a Ciência e a Tecnologia (FCT

Handling boundary constraints for particle swarm optimization in high-dimensional search space

Despite the fact that the popular particle swarm optimizer (PSO) is currently being extensively applied to many real-world problems that often have high-dimensional and complex fitness landscapes, the effects of boundary constraints on PSO have not attracted adequate attention in the literature. However, in accordance with the theoretical analysis in [11], our numerical experiments show that particles tend to fly outside of the boundary in the first few iterations at a very high probability in high-dimensional search spaces. Consequently, the method used to handle boundary violations is critical to the performance of PSO. In this study, we reveal that the widely used random and absorbing bound-handling schemes may paralyze PSO for high-dimensional and complex problems. We also explore in detail the distinct mechanisms responsible for the failures of these two bound-handling schemes. Finally, we suggest that using high-dimensional and complex benchmark functions, such as the composition functions in [19], is a prerequisite to identifying the potential problems in applying PSO to many real-world applications because certain properties of standard benchmark functions make problems inexplicit. © 2011 Elsevier Inc. All rights reserved