This manuscript contains an outline of lectures course"Evolutionary Algorithms"read by the author in Omsk State University n.a. F.M.Dostoevsky. The course covers Canonic Genetic Algorithm and various other genetic algorithms as well as evolutionary strategies, genetic programming, tabu search and the class of evolutionary algorithms in general. Some facts, such as the Rotation Property of cross...

This paper proposes a technique for visualizing the progression of evolutionary algorithms such as genetic algorithms (GAs). Our technique supposes that a GA solves optimization problems that maximize target functions with multiple parameters. Then, our technique presents the timevarying parameters during the progression of the GA. The technique applies two-tone pseudo-coloring for the precise ...

This paper proposes a method that combines tournament selectionbased genetic algorithms (GA) with simulated annealing (SA) to improve the performance of GA. Furthermore random signal-based learning (RSBL) is added as an additional GA operator to refine the solutions after mutation. The fuzzy controller for the inverted pendulum is used to verify the validity of the proposed method.

Computing the bandwidth-delay-constrained least-cost multicast routing tree is an NP-complete problem. In this paper, we propose a novel QoS-based multicast routing algorithm based on the genetic algorithms (GA). In the proposed algorithm, the connectivity matrix of edges is used for genotype representation. Some novel heuristics are also proposed for mutation, crossover, and creation of random...

Information Retrieval (IR) deals with searching, retrieving and presenting information within the WWW and online databases and also searches the web documents. Genetic Algorithms (GA) are robust and efficient search and optimization techniques inspired by the Darwin's theory of natural evolution. In this paper, the applicability of Genetic algorithm (GA) in the field of information retrieval an...

This paper proposes a genetic weighted K-means algorithm called GWKMA, which is a hybridization of a genetic algorithm (GA) and a weighted K-means algorithm (WKMA). GWKMA encodes each individual by a partitioning table which uniquely determines a clustering, and employs three genetic operators (selection, crossover, mutation) and a WKMA operator. The superiority of the GWKMA over the WKMA and o...

A method for solving satisfiability (SAT) instances is presented. It is based on two basic components: An Epistasis Reducer Algorithm (ERA) and a simple Genetic Algorithm (GA). ERA is implemented by a simulated annealing algorithm (SA), which preprocess the original SAT problem by rearranging the variables to satisfy the condition that the most related ones are in closer positions inside the ch...

This paper addresses an attempt to evolve genetic algorithms by a particular genetic programming method to make it able to solve the classical Job Shop Scheduling problem (JSSP), which is a type of very well known hard combinatorial optimisation problems. The aim is to look for a better GA such that solves JSSP with preferable scores. This looking up procedure is done by evolving GA with GP. Fi...

This paper proposes an intelligent trading system using support vector regression optimized by genetic algorithms (SVR-GA) and multilayer perceptron optimized with GA (MLP-GA). Experimental results show that both approaches outperform conventional trading systems without prediction and a recent fuzzy trading system in terms of final equity and maximum drawdown for Hong Kong Hang Seng stock index.

This paper examines the use of genetic algorithms (GAs) to solve the school timetabling problem. The school timetabling problem falls into the category of NP-hard problems. Instances of this problem vary drastically from school to school and country to country. Previous work in this area has used genetic algorithms to solve a particular school timetabling problem and has not evaluated the perfo...