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

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

A brief overview of the synergy between metaheuristics and unconventional dynamics

This brief review paper focuses on the modern and original hybridization of the unconventional dynamics and the metaheuristic optimization algorithms. It discusses the concept of chaos-based optimization in general, i.e. the influence of chaotic sequences on the population diversity as well as at the metaheuristics performance. Further, the non-random processes used in evolutionary algorithms, and finally also the examples of the evolving complex network dynamics as the unconventional tool for the visualization and analysis of the population in popular optimization metaheuristics. This work should inspire the researchers for applying such methods and take advantage of possible performance improvements for the optimization tasks. © Springer Nature Switzerland AG 2020

Randomization of individuals selection in differential evolution

This research deals with the hybridization of two computational intelligence fields, which are the chaos theory and evolutionary algorithms. Experiments are focused on the extensive investigation on the different randomization schemes for selection of individuals in differential evolution algorithm (DE). This research is focused on the hypothesis whether the different distribution of different pseudo-random numbers or the similar distribution additionally enhanced with hidden complex chaotic dynamics providing the unique sequencing are more beneficial to the heuristic performance. This paper investigates the utilization of the two-dimensional discrete chaotic systems, which are Burgers and Lozi maps, as the chaotic pseudo-random number generators (CPRNGs) embedded into the DE. Through the utilization of either chaotic systems or equal identified pseudo-random number distribution, it is possible to entirely keep or remove the hidden complex chaotic dynamics from the generated pseudo random data series. This research utilizes set of 4 selected simple benchmark functions, and five different randomizations schemes; further results are compared against canonical DE. © Springer Nature Switzerland AG 2019

L'analyse comparée des savoirs enseignés en natation dans les classes de 6ème des collèges et lycées du Bénin et de la France

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

Population diversity analysis in adaptive differential evolution variants with unconventional randomization schemes

This research represents a detailed insight into the modern and popular hybridization of unconventional quasiperiodic/chaotic sequences and evolutionary computation. It is aimed at the influence of different randomization schemes on the population diversity, thus on the performance, of two selected adaptive Differential Evolution (DE) variants. 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 unconventional chaotic pseudo-random number generators. The population diversity is recorded for 15 test functions from the CEC 2015 benchmark set in 10D. © 2019, Springer Nature Switzerland AG.Ministry 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/2019/002]; COST (European Cooperation in Science & Technology) under Action (ImAppNIO) [CA15140]; COST (European Cooperation in Science & Technology) under Action (cHiPSet) [IC1406]; VSB-Technical University of Ostrava [SGS 2019/137

On the tuning of complex dynamics embedded into differential evolution

This research deals with the hybridization of the two soft-computing fields, which are chaos theory and evolutionary computation. This paper aims on the experimental investigations on the chaos-driven evolutionary algorithm Differential Evolution (DE) concept. This research represents the continuation of the satisfactory results obtained by means of chaos embedded (driven) DE, which utilizes the chaotic dynamics in the place of pseudorandom number generators This work is aimed at the tuning of the complex chaotic dynamics directly injected into the DE. To be more precise, this research investigates the influence of different parameter settings for discrete chaotic systems to the performance of DE. Repeated simulations were performed on the IEEE CEC 13 benchmark functions set in dimension of 30. Finally, the obtained results are compared with canonical DE and jDE. © Springer International Publishing Switzerland 2015.MSMT-7778/2014, NPU, Northwestern Polytechnical Universit