Do evolutionary algorithms indeed require random numbers? Extended study

An 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 are random processes really 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 a certain class of deterministic processes can be used instead of random number generators without lowering of evolutionary algorithms performance. © Springer International Publishing Switzerland 2013

Chaos driven particle swarm optimization with basic particle performance evaluation – An initial study

In this paper, the novel concept of particle performance evaluation is introduced into the chaos driven particle swarm optimization algorithm (PSO). The discrete chaotic dissipative standard map is used here as a chaotic pseudorandom number generator (CPRNG). In the novel proposed particle performance evaluation method the contribution of each particle to the process of obtaining the global best solution is investigated periodically. As a reaction to the possible poor performance of a particular particle, its velocity calculation is thereafter altered. Through utilization of this approach the convergence speed and overall performance of PSO algorithm driven by CPRNG based on Dissipative map is improved. The proposed method is tested on the CEC13 benchmark set with two different dimension settings

How unconventional chaotic pseudo-random generators influence population diversity in differential evolution

This research focuses on the modern hybridization of the discrete chaotic dynamics and the evolutionary computation. It is aimed at the influence of chaotic sequences on the population diversity as well as at the algorithm performance of the simple parameter adaptive Differential Evolution (DE) strategy: jDE. Experiments are focused on the extensive investigation of totally ten different randomization schemes for the selection of individuals in DE algorithm driven by the default pseudo random generator of Java environment and nine different two-dimensional discrete chaotic systems, as the chaotic pseudo-random number generators. The population diversity and jDE convergence are recorded for 15 test functions from the CEC 2015 benchmark set in 30D. © Springer International Publishing AG, part of Springer Nature 2018.2018/177; IC406; MSMT-7778/2014, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; LO1303, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; 710577, Horizon 2020; CA15140; IGA/CebiaTech/2018/003; CZ.1.05/2.1.00/03.0089, FEDER, European Regional Development FundMinistry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST ActionEuropean Cooperation in Science and Technology (COST) [CA15140, IC406]; SGS [2018/177]; VSB-TUO; EU's Horizon 2020 research and innovation programme [710577

On relation between swarm and evolutionary dynamics and complex networks

This paper is an introduction to a novel method for visualizing the dynamics of evolutionary algorithms in the form of networks. The whole idea is based on the obvious similarity between interactions between individuals in a swarm and evolutionary algorithms and for example, users of social networks, linking between web pages, etc. In this paper, two completely different areas of research are merged: (complex) networks and evolutionary computation. As already mentioned, interactions among the individuals in a swarm and evolutionary algorithms can be considered like user interactions in social networks or just people in society. This induces hypothesis whether interactions inside of EAs can be taken like interactions in society or swarm colonies. The analogy between individuals in populations in an arbitrary evolutionary algorithm and vertices of a network is discussed, as well as between edges in a network and communication between individuals in a population. © Springer Nature Switzerland AG 2019

Chaos powered grammatical evolution

In this paper we discuss alternative nonrandom generators for symbolic regression algorithms and compare its variants powered by classical pseudo-random number generator and chaotic systems. Experimental data from previous experiments reported for genetic programming and analytical programming is used. The selected algorithms are differential evolution and SOMA. Particle swarm, simulated annealing and evolutionary strategies are in process of investigation. All of them are mutually used in scheme Master-Slave meta-evolution for final complex structure fitting and its parameter estimation.Agency of the Czech Republic-GACR [P103/13/08195S]; SGS [SP2014/159]; VSB-Technical University of Ostrava, Czech Republic; European Regional Development Fund [CZ.1.05/2.1.00/03.0089

Le consommateur golfeur : perception et réduction du risque perçu lié à l'achat de l'abonnement

Enquête par questionnaire (en annexe) réalisée auprès de 298 consommateurs golfeurs, sur les risques perçus par le client d'un club de golf lors de l'achat d'un abonnement (les abonnements permettent de réaliser 50% des recettes d'un club). Avec une revue de la littérature portant sur le concept de risque perçu en marketing et sur les analyses des comportements de consommation des joueurs de gol