The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised fur...

This paper presents an evolution-based approach for solving multiple molecular sequence alignment. The approach is based on the island parallel genetic algorithm that relies on the fitness distribution over the population of alignments. The algorithm searches for an alignment among the independent isolated evolving populations by optimizing weighted sum of pairs objective function which measure...

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entr...

Application of genetic algorithms to problems where the fitness landscape changes dynamically is a challenging problem. Genetic algorithms for such environments must maintain a diverse population that can adapt to the changing landscape and locate better solutions dynamically. A niching genetic algorithm suitable for locating multiple solutions in a multimodal landscape is applied. The results ...

This thesis proposes a methodology based on genetic algorithms for inferring finite state machines. It defines basic parameters like fitness functions and chromosomal representation that can be used with canonical genetic algorithm. Different types of finite state machines can be infered. Several improvements were introduced: inner decimal representation has been used to improve the speed of in...

Height fields are evolved for use in virtual environments. Interactive aesthetic selection is employed as a fitness function for generating successive populations of images with a genetic algorithm. The images are represented using continuous layered pattern functions, which are based on procedural texturing techniques. The design space defined by the representation can be controllably biased t...

The talk describes a general approach of a genetic algorithm for multiple objective optimization problems. A particular dominance relation between the individuals of the population is used to define a fitness operator, enabling the genetic algorithm to address even problems with efficient, but convexdominated alternatives. The algorithm is implemented in a multilingual computer program, solving...

-In this paper we glance through the various approaches used by the researchers to develop an automatic timetable using Genetic algorithms. The optimized genetic algorithm can be used with the heuristic approach to design and develop the timetable of an institute. At stake during the process of development, the stakeholders are the professors and the students. The efficient utilization of the i...

neural networks solving approximation problem. It is based on two hierarchical evolutionary algorithms with multiobjective Pareto optimisation. The lower level algorithm searches for rules that are optimised by the upper level algorithm. The conclusion of the rule takes the form of a tree whose inner nodes contain functions and operators, and leaves—identifiers of attributes and numeric constan...

This papers describes an evolutionary algorithm based on a statistical representation of populations of individuals. Experiments on robot navigation and on numerical fitness functions are presented in order to measure the performance of the algorithm compared to traditional genetic algorithms. Results show that the method is suitable for onboard online evolution because it requires low amount o...