Pavel Osmera

Parallel Grammatical Evolution for Circuit Optimization

This paper describes application of Parallel Grammatical Evolution (PGE) algorithm to combinatorial logic circuit generation. The grammar and algorithms used are described. To increase the efficiency of Grammatical Evolution (GE) the backward processing algorithm was used. Different approaches to create multiobjective fitness functions are described and tested. Specifically the fitness functions are defined as set of rules incorporating different comparison methods in each stage of the computation. The algorithm is internally parallel and consists of three different interconnected populations.

Transplant Evolution for Optimization of General Controllers

This paper describes a new method of evolution that is named Transplant Evolution (TE). None of the individuals of the transplant evolution contains genotype. Each individual of the transplant evolution contains only phenotype. Reproduction methods as crossover and mutation work and store only the phenotype. The hierarchical structure of grammar-differential evolution that is used for finding optimal structures and parameters of general controllers is described.

Evolution of Complexity

The strength of physical science lies in its ability to explain phenomena as well as make prediction based on observable, repeatable phenomena according to known laws. Science is particularly weak in examining unique, nonrepeatable events. We try to piece together the knowledge of evolution with the help of biology, informatics, and physics to describe a complex evolutionary structure with unpredictable behavior. Evolution is a procedure where matter, energy, and information come together. Our research can be regarded as a natural extension of Darwin’s evolutionary view of the last century. We would like to find plausible uniformitarian mechanisms for evolution of complex systems. Workers with specialized training in overlapping disciplines can bring new insights to an area of study, enabling them to make original contributions. This chapter describes evolution of complexity as a basic principle of evolutionary computation.

Two Level Parallel Grammatical Evolution

This paper describes a two level parallel grammatical evolution (TLPGE) that can evolve complete programs using a variable length linear genome to govern the mapping of a Backus Naur Form grammar definition. To increase the efficiency of grammatical evolution (GE) the influence of backward processing was tested and a second level with differential evolution was added. The significance of backward coding (BC) and the comparison with standard coding of GEs is presented. The new method is based on parallel grammatical evolution (PGE) with a backward processing algorithm, which is further extended with a differential evolution algorithm. Thus a two-level optimization method was formed in attempt to take advantage of the benefits of both original methods and avoid their difficulties. Both methods used are discussed and the architecture of their combination is described. Also application is discussed and results on a real-word application are described.

GA with fuzzy inference system

Applications of genetic algorithms (GA) for optimisation problems are widely known as well as their advantages and disadvantages compared with classical numerical methods. In practical tests, GA appears a robust method with a broad range of applications. The determination of GA parameters could be complicated. Therefore for some real-life applications, several empirical observations of an experienced expert are needed to define these parameters. This fact degrades the applicability of a GA for most of the real-world problems and users. Therefore, this article discusses some possibilities with setting GA parameters. The setting method of GA parameters is based on the fuzzy control of values of GA parameters. The feedback for the fuzzy control of GA parameters is realized by virtue of the behavior of some GA characteristics. The goal of this article is to present the conception of the solution and some new ideas.

Ring structures of atoms and molecules

Knowledge of structure rules of the atomic nucleus and the properties of vortex electromagnetic field allow us to create relatively precisely the structures of individual atoms and molecules. Properties of atoms are largely described by the structure of their electron shells. However, the standard model of atoms does not allow define this structure exactly. New theory VFRT (vortex-fractal-ring-theory) can solve this lack. Theory VFRT uses fractal ring structure of the electron, the proton and the neutron, and can describe the inner structure of atomic nuclei. Fractal descriptions of Nature are very promising. The atomic nucleus can be built from the ring protons and neutrons. This new theory assumes that the arrangement of electron shells arises from the structure of the atomic nucleus. Electrons are not in orbit around the atomic nucleus, but each electron levitates with the corresponding proton of the nucleus. The levitation bond between the electron and the proton is formed by an electromagnetic vortex structure. Theory VFRT expands understanding of nature through a new perspective on the evolution of lifeless nature using a vortex, fractal and ring substructures with self-organization, from quarks, electrons, protons and neutrons, atoms, molecules, to the structure of complex organic compounds.

Vortex‐ring‐fractal Structure of Atom and Molecule

This chapter is an attempt to attain a new and profound model of the nature’s structure using a vortex‐ring‐fractal theory (VRFT). Scientists have been trying to explain some phenomena in Nature that have not been explained so far. The aim of this paper is the vortex‐ring‐fractal modeling of elements in the Mendeleev’s periodic table, which is not in contradiction to the known laws of nature. We would like to find some acceptable structure model of the hydrogen as a vortex‐fractal‐coil structure of the proton and a vortex‐fractal‐ring structure of the electron. It is known that planetary model of the hydrogen atom is not right, the classical quantum model is too abstract. Our imagination is that the hydrogen is a levitation system of the proton and the electron. Structures of helium, oxygen, and carbon atoms and a hydrogen molecule are presented too.

Combined Heuristic Approach to Resource-Constrained Project Scheduling Problem

This chapter deals with the resource-constrained project scheduling problem that belongs to NP-hard optimisation problems. There are many different heuristic strategies how to shift activities in time when resource requirements exceed their available amounts. We propose a transformation of the problem to a sequence of simpler instances of (multi)knapsack problems that do not use traditionally predefined activity priorities and enable to maximise limited resources in all time intervals given by start or end of an activity and therefore to reduce the total time.

Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period

The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. The new approach to analysis of on-line identification methods based on one-step-ahead prediction clears up their sensitivity to disturbances in control loop. On one hand faster disturbance rejection due to short sampling period can be an advantage but on the other hand it brings us some practical problems. Particularly, quantization error and finite numerical precision of industrial controller must be considered in the real process control. We concentrate our attention on dealing with adverse effects that work on real-time identification of process, especially quantization. It is shown; that a neural network applied to on-line identification process produces more stable solution in the rapid sampling domain

Evolvable controllers with hierarchical structure

We are trying to piece together the knowledge of evolution with the help of biology, informatics and physics to create complex evolutionary algorithms with a parallel and hierarchical structure. It can speed up the creation of optimization algorithms with high quality features. The adaptive significance of genetic algorithms (GAs) with diploid chromosomes and an artificial immune system has been studied. An artificial immune system was designed to support the parallel evolutionary algorithms. We implemented hybrid and parallel genetic algorithms for design of evolvable controllers. A flexible hierarchical structure with PID, fuzzy and neural controllers can be designed by parallel evolutionary algorithms. The adaptive significance of parallel GAs and the comparison with standard GAs are presented.