Marin Golub

Solving timetable scheduling problem using genetic algorithms

A genetic algorithm for solving a timetable scheduling problem is described. The algorithm was tested on small and large instances of the problem. Algorithm performance was significantly enhanced with modification of basic genetic operators. Intelligent operators restrain the creation of new conflicts in the individual and improve the overall algorithm s behavior.

Comparison of Heuristic Algorithms for the N-Queen Problem

This paper addresses the way in which heuristic algorithms can be used to solve the n-queen problem. Metaheuristics for algorithm simulated annealing, tabu search and genetic algorithm are shown, test results are demonstrated and upper bound complexity is determined. The efficiencies of algorithms are compared and their achievements are measured. Due to the reduction of the fitness function complexity to O(1) problem instances with large dimensions are solved.

Evaluation of crossover operator performance in genetic algorithms with binary representation

Genetic algorithms (GAs) generate solutions to optimization problems using techniques inspired by natural evolution, like crossover, selection and mutation. In that process, crossover operator plays an important role as an analogue to reproduction in biological sense. During the last decades, a number of different crossover operators have been successfully designed. However, systematic comparison of those operators is difficult to find. This paper presents a comparison of 10 crossover operators that are used in genetic algorithms with binary representation. To achieve this, experiments are conducted on a set of 15 optimization problems. A thorough statistical analysis is performed on the results of those experiments. The results show significant statistical differences between operators and an overall good performance of uniform, single-point and reduced surrogate crossover. Additionally, our experiments have shown that orthogonal crossover operators perform much poorer on the given problem set and constraints.

Evolving cryptographically sound boolean functions

This paper explores the evolution of Boolean functions for a cryptographic usage, with genetic algorithms and genetic programming. We also experiment with a new mutation operator and a new kind of initialization process. Results obtained show that those modifications can help in obtaining better solutions. The results indicate that it is possible to obtain high quality Boolean functions with algorithms that are not tailor-made for this purpose. Additionally, among the algorithms tested, the best performance was obtained with variations of genetic programming.

Solving n-Queen problem using global parallel genetic algorithm

This paper shows a way in which genetic algorithms can be used to solve the n-Queen problem. Custom chromosome representation, evaluation function and genetic operators are presented. A global parallel genetic algorithm is also demonstrated as a possible way to increase GA speed. Results are shown for several large values of n and several conclusions are drawn about solving NP problems with genetic algorithms.

On the recombination operator in the real-coded genetic algorithms

Crossover is the most important operator in real-coded genetic algorithms. However, the choice of the best operator for a specific problem can be a difficult task. In this paper we compare 16 crossover operators on a set of 24 benchmark functions. A detailed statistical analysis is performed in an effort to find the best performing operators. The results show that there are significant differences in efficiency of different crossover operators, and that the efficiency may also depend on the distinctive properties of the fitness function. Additionally, the results point out that the combination of crossover operators yields the best results.

Evolutionary algorithms for the resource constrained scheduling problem

This paper investigates the use of evolutionary algorithms for solving resource constrained scheduling problem which belongs to the class of NP complete problems. The problem involves finding optimal sequence of activities with given resource constraints. Evolutionary algorithms used in this paper are genetic algorithms and genetic programming, for which adequate scheduling mechanisms are defined. Presented solutions are compared with existing heuristics or optimal results.

The information systems' security level assessment model based on an ontology and evidential reasoning approach

In the area of information technology an amount of security issues persists through time. Ongoing activities on security solutions aim to integrate existing security guidelines, best practices, security standards and existing solutions, but they often lack a knowledge base or do not involve all security issues, particularly human influence.In this paper, we presented a model that can be the basis for a novel information systems security evaluation solution. This solution should be able to cover a wide range of all possible information security issues. Our model is based on an OWL ontology for knowledge base, uses an enhanced Evidential Reasoning algorithm for mathematical calculations and possesses a simple reflex intelligent agents algorithm as a decision supporting element.Properties for this model supervene from properties of its constructing elements. Knowledge base being built on OWL ontology is a major element of the model. It can provide high flexibility and applicability to different information systems and business organizations; upgradeability to be up to date regarding current security issues and new threats; and high versatility, taking into evaluation all possible aspects regarding security issues, e.g., network security, software and hardware issues, human influence, security policies and disaster recovery plans. Enhanced Evidential Reasoning algorithm is based on the Dumpster-Shafer theory and is well suited for calculations with experts subjective judgements combining qualitative with quantitative evaluation grades. We designed an algorithm for back coupling based on a simple reflex intelligent agent for results presentation and decision support.In our work, we explained how to connect and use each of the models constructive elements to obtain information security evaluation results. In addition, we conducted a case study with the proposed model on a small business organization. To test our model, we also used the standard qualitative risk assessment method on the same business organization in order to compare both qualitative results.Preliminary testing results have shown that the presented model could achieve its goal if it would be developed into an integrated software tool with a well-defined and up-to-date ontological knowledge base.

Exam timetabling using genetic algorithm

In this paper we present a case study concerning the exam timetabling problem we faced, and its genetic algorithm based solution. Several variations of the algorithm as well as the influence of algorithm parameters are analyzed.

Asynchronous and implicitly parallel evolutionary computation models

This paper presents the design and the application of asynchronous models of parallel evolutionary algorithms. An overview of the existing parallel evolutionary algorithm (PEA) models and available implementations is given. We present new PEA models in the form of asynchronous algorithms and implicit parallelization, as well as experimental data on their efficiency. The paper also discusses the definition of speedup in PEAs and proposes an appropriate speedup measurement procedure. The described parallel EA algorithms are tested on problems with varying degrees of computational complexity. The results show good efficiency of asynchronous and implicit models compared to existing parallel algorithms.