Ivan Zelinka

Analytical Programming - a Novel Approach for Evolutionary Synthesis of Symbolic Structures

This chapter discusses an alternative approach for symbolic structures and solutions synthesis and demonstrates a comparison with other methods, for example Genetic Programming (GP) or Grammatical Evolution (GE). Generally, there are two well known methods, which can be used for symbolic structures synthesis by means of computers. The first one is called GP and the other is GE. Another interesting research was carried out by Artificial Immune Systems (AIS) or/and systems, which do not use tree structures like linear GP and other similar algorithm like Multi Expression Programming (MEP), etc. In this chapter, a different method called Analytic Programming (AP), is presented. AP is a grammar free algorithmic superstructure, which can be used by any programming language and also by any arbitrary Evolutionary Algorithm (EA) or another class of numerical optimization method. This chapter describes not only theoretical principles of AP, but also its comparative study with selected well known case examples from GP as well as applications on synthesis of: controller, systems of deterministic chaos, electronics circuits, etc. For simulation purposes, AP has been co-joined with EA’s like Differential Evolution (DE), Self-Organising Migrating Algorithm (SOMA), Genetic Algorithms (GA) and Simulated Annealing (SA). All case studies has been carefully prepared and repeated in order to get valid statistical data for proper conclusions. The term symbolic regression represents a process during which measured data sets are fitted, thereby a corresponding mathematical formula is obtained in an analytical way. An output

Do evolutionary algorithms indeed require randomness

Inherent part of evolutionary algorithms that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes. In this participation we discuss whether random processes really are needed in evolutionary algorithms. We use n periodic deterministic processes instead of random number generators and compare performance of evolutionary algorithms powered by those processes and by pseudo-random number generators. Deterministic processes used in this participation are based on deterministic chaos and are used to generate periodical series with different length. Results presented here are numerical demonstration rather than mathematical proofs. We propose that certain class of deterministic processes can be used instead of random number generators without lowering the performance of evolutionary algorithms.

Designing PID Controller For DC Motor System By Means Of Enhanced PSO Algorithm With Discrete Chaotic Lozi Map.

The main aim of this paper is the utilization of discrete chaotic Lozi map based chaos number generator to enhance the performance of PSO algorithm. This paper presents the results of research, in whichchaos enhanced PSO algorithm is used to design an optimal PID controller for DC motor system. Obtained results are compared with other non-heuristic and heuristic methods.

Investigation on the Differential Evolution driven by selected six chaotic systems in the task of reactor geometry optimization

In this paper, Differential Evolution (DE) is used in the task of optimization of batch reactor geometry. The novality of the approach is that the six selected discrete dissipative chaotic maps are used as the chaotic pseudo random number generator to drive the mutation and crossover process in the DE. The optimized results obtained are compared with original reactor geometry and process parameters adjustment. The statistical analysis of the results given by six versions of chaos driven DE is compared with canonical DE strategy.

Investigation On Evolutionary Edtas Chaos Control

This work deals with an investigation on optimization of the feedback control of chaos based on using of the evolutionary algorithms. The main aim of this work is to show that evolutionary algorithms are capable of optimization of chaos control. As a model of deterministic chaotic system the Henon map was used. The optimizations were realized in several ways, each one for another set of parameters of evolution algorithms or another cost functions. The evolutionary algorithm SOMA (Self-Organizing Migrating Algorithm) was used in four versions. For each version simulations were repeated several times to show and check robustness of used method.

New Adaptive Approach for Chaos PSO Algorithm Driven Alternately by Two Different Chaotic Maps – An Initial Study

In this initial study a novel adaptive approach for chaos driven PSO algorithm is proposed. Two different chaotic maps are used as pseudorandom number generators and switched over during the run of chaos driven PSO algorithm. The new adaptive approach brings promising results that are presented and briefly analyzed.

Design Of Targeting Cost Function For Evolutionary Optimization Of Chaos Control

This contribution deals with optimization of the control of chaos by means of evolutionary algorithms. The main aim of this work is to show that evolutionary algorithms are capable of optimization of chaos control and to show several methods of constructing the complex targeting cost function leading to satisfactory results. As a model of deterministic chaotic system the two dimensional Henon map was used. The optimizations were realized in several ways, each one for another cost function or another desired periodic orbit. The evolutionary algorithm Self-Organizing Migrating Algorithm (SOMA) was used in four versions. For each version, simulations were repeated several times to show and check robustness of used method and cost function. At the end of this work the results of optimized chaos control for each designed targeting cost function are compared.

Study On Swarm Dynamics Converted Into Complex Network.

In this study it is presented a summarization of our research of possible ways of creating of complex networks from the inner dynamics of Swarm Intelligence based algorithms. The particle swarm optimization algorithm and the firefly algorithm are studied in this paper. Several methods of complex network creation are proposed and discussed alongside with possibilities for future research and application.

StarCraft: Brood War — Strategy powered by the SOMA swarm algorithm

This participation is focused on artificial intelligence techniques and their practical use in computer game. The aim is to show how program (based on evolutionary algorithms) can replace a man in the strategy game StarCraft: Brood War. Implementation used in our experiments use classic techniques of artificial intelligence environments, as well as unconventional techniques, such as evolutionary computation. An artificial player, proposed in this paper, is the combination of the decision tree and evolutionary algorithm SOMA. Whole code for experiments was written in the Java programming language. The proposed code provides a simple implementation of the artificial computer player in combination with slightly modified algorithm SOMA. This provides an opportunity for effective, coordinated movement of combat units around the combat landscape. Research reported here has shown potential benefit of evolutionary computation in the field of strategy games.

Randomness and Chaos in Genetic Algorithms and Differential Evolution

Evolutionary methods and stochastic algorithms in general rely heavily on streams of (pseudo-)random numbers generated in course of their execution. The pseudo-random numbers are utilized for in-silico emulation of probability-driven natural processes such as modification of genetic information (mutation, crossover), partner selection, and survival of the fittest (selection, migration). Deterministic chaos is a very well known mathematical concept that can be used to generate sequences of real numbers within selected interval. In the past, it has been used as a basis for various pseudo-random number generators with interesting properties. This work provides an empirical comparison of the performance of genetic algorithms and differential evolution using different pseudo-random number generators and chaotic systems as sources of stochasticity.