Roman Jasek

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

Discrete Self-Organising Migrating Algorithm for flow-shop scheduling with no-wait makespan

Abstract This paper introduces a novel Discrete Self-Organising Migrating Algorithm for the task of flow-shop scheduling with no-wait makespan. The new algorithm is tested with the small and medium Taillard benchmark problems and the obtained results are competitive with the best performing heuristics in the literature.

Preliminary investigation on relations between complex networks and evolutionary algorithms dynamics

In this article we discuss relations between the so-called complex networks and dynamics of evolutionary algorithms. The main aim of this article is to investigate whether it is possible to model (or vizualize) evolutionary dynamics as complex networks, whose connections will represent interactions amongst the individuals during all generations. Our simulations are based on selected evolutionary algorithms (2 algorithms in 6 versions) and test functions (4 out of 17). Data obtained through the simulations were processed graphically as well as statistically.

Performance of Chaos Driven Differential Evolution on Shifted Benchmark Functions Set

This research deals with the extended investigations on the concept of a chaos-driven evolutionary algorithm Differential Evolution (DE). This paper is aimed at the embedding of set of six discrete dissipative chaotic systems in the form of chaos pseudo random number generator for DE. Repeated simulations were performed on the set of two shifted benchmark test functions in higher dimensions. Finally, the obtained results are compared with canonical DE.

Usage of Modern Exponential-Smoothing Models in Network Traffic Modelling

The article summarized current state of our works regarding usage of exponential smoothing Holt-Winters’ based models for analysis, modelling and forecasting Time Series with data of computer network traffic. Especially we use two models proposed by J. W. Taylor to deal with double and triple seasonal cycles for modelling network traffic in two local area networks and three campus networks. We use three time series with data of TCP, UDP and ICMP traffic (given by number of packets per interval) on each network.

SYNTHESIS OF FEEDBACK CONTROLLER FOR CHAOTIC SYSTEMS BY MEANS OF EVOLUTIONARY TECHNIQUES

This research deals with a synthesis of control law for three selected discrete chaotic systems by means of analytic programming. The novality of the approach is that a tool for symbolic regression—analytic programming—is used for such kind of difficult problem. The paper consists of the descriptions of analytic programming as well as chaotic systems and used cost function. For experimentation, Self‐Organizing Migrating Algorithm (SOMA) with analytic programming was used.

Application of Analytic Programming for Evolutionary Synthesis of Control Law—Introduction of Two Approaches

This research deals with an evolutionary synthesis of control law for Logistic equation, which is a discrete chaotic system. The novelty of the research is that an Analytic Programming (AP), which is a tool for symbolic regression, is used for the synthesis of feedback controller for chaotic system. This work introduces and compares two approaches representing blackbox type cost function, as well as not-blackbox type cost function. These two approaches are used for the purpose of stabilisation of the higher periodic orbits, which stand for oscillations between several values of chaotic system. The work consists of the descriptions of analytic programming as well as chaotic system and used cost functions. For experimentation, Self-Organising Migrating Algorithm (SOMA) and Differential Evolution (DE) were used.

Optimization of Artificial Neural Network Structure in the Case of Steganalysis

This research introduces a method of steganalysis by means of neural networks and its structure optimization. The main aim is to explain the approach of revealing a hidden content in jpeg files by feed forward neural network with Levenberg-Marquardt training algorithm. This work is also concerned to description of data mining techniques for structure optimization of used neural network. The results showed almost 100% success of detection.

Usability of Software Intrusion-Detection System in Web Applications

This article is focused on the security solution based on intrusion detection idea, which should be independent of the web server type or configuration and do not rely on the other network hardware components. Discussed intrusion detection system solution is connected directly with the web application and is based on the real-time request analysis. The main opportunities of proposed principle are very low cost and simple implementation. Proposal is based on implementation of LGPL library PHPIDS [https://phpids.org/] into the demo application which consists of simple web form for testing. Integration of PHPIDS library was tested against the main web security flaws - SQL Injection, Cross Site Scripting, and HTTP Parameter Pollution. On this demo application, simple stress tests were performed and also level of security was evaluated. Moreover, suggestions for future improvements of this security solution are discussed.

Clustered enhanced differential evolution for the blocking flow shop scheduling problem

A novel clustered population paradigm is presented in this paper which is based on Chaos principles of edges and attractors. Convergence in evolutionary algorithms is viewed as a manifestation through cyclic dynamics and thus a new population is developed which is clustered and separated through new segregation bias rules. This population is embedded on the Enhanced Differential Evolution and the flow shop scheduling problem with blocking is solved. The two flow shop benchmark problems of Rec/Car/Hel and Taillard are solved with this new approach and the results favorably compared with published results in literature. A total of 49 new upper bounds for the Taillard problems was obtained during experimentation.