Analytical Programming - a Novel Approach for Evolutionary Synthesis of Symbolic Structures

Zelinka, Ivan; Davendra, Donald; Šenkeřík, Roman; Jašek, Roman; Oplatková, Zuzana Komínková

doi:10.5772/16166

Cited by 58 publications

(27 citation statements)

References 23 publications

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“…Our aim is to try algorithms like scatter search [10], evolutionary strategies [11], genetic algorithms [12], [28] or particle swarm [13]. Also novel algorithms will be tested for its performance under our proposed approach in [26], [27] and alternative methods of symbolic regression [29].…”

Section: Resultsmentioning

confidence: 99%

Do Evolutionary Algorithms Indeed Require Random Numbers? Extended Study

Zelinka

Chadli

Davendra

et al. 2013

Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems

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Abstract. 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.

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Section: Resultsmentioning

confidence: 99%

Do Evolutionary Algorithms Indeed Require Random Numbers? Extended Study

Zelinka

Chadli

Davendra

et al. 2013

Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems

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“…The initial idea of symbolic regression by means of a computer program was proposed in GP (Koza, 1990;Koza, 1998). The other approach of GE was developed in (Ryan et al, 1998) and AP in (Zelinka et al, 2011). Another interesting investigation using symbolic regression were carried out (Johnson, 2004) on AIS and Probabilistic Incremental Program Evolution (PIPE), which generates functional programs from an adaptive probability distribution over all possible programs.…”

Section: Big Data and Evolutionary Algorithms -Perspectives And Possimentioning

confidence: 99%

“…Individuals in the shape of a tree, or formula like 0.234Z + X − 0.789, are called programs. Because GP is based on GA, evolutionary steps (mutation, crossover, ...) in GP are in principle the same as GA, see (Koza, 1990;Koza et al, 1998;Zelinka, 2011).…”

Section: Genetic Programmingmentioning

confidence: 99%

“…These are transformed into integer sequences and then mapped into a final program in the Backus-Naur form, the rule used for individuals transforming into a program is based on operation modulo, see (O'Neill and Ryan, 2003). Codons are then transformed into an integer domain (in the range of values in 0-255) and by means of defined grammar it is transformed into appropriate structure, see (O'Neill and Ryan, 2003;Zelinka et al, 2011).…”

Section: Grammatical Evolutionmentioning

confidence: 99%

“…The last method described here is called Analytic programming (AP), see (Zelinka et al, 2011), which has been compared to GP with very good results (see, e.g., (Zelinka and Oplatkova, 2003;Zelinka et al, 2005;Oplatkova and Zelinka, 2006;Zelinka et al, 2008)). …”

Section: Analytic Programmingmentioning

confidence: 99%

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Big Data Movement: A Challenge in Data Processing

Pokorný

Škoda

Zelinka³

et al. 2015

Studies in Big Data

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Abstract. This chapter discusses modern methods of data processing, especially data parallelization and data processing by bio-inspired methods. The synthesis of novel methods is performed by selected evolutionary algorithms and demonstrated on the astrophysical data sets. Such approach is now characteristic for so called Big Data and Big Analytics. First, we describe some new database architectures that support Big Data storage and processing. We also discuss selected Big Data issues, specifically the data sources, characteristics, processing, and analysis. Particular interest is devoted to parallelism in the service of data processing and we discuss this topic in detail. We show how new technologies encourage programmers to consider parallel processing not only in a distributive way (horizontal scaling), but also within each server (vertical scaling). The chapter also intensively discusses interdisciplinary intersection between astrophysics and computer science, which has been denoted astroinformatics, including a variety of data sources and examples. The last part of the chapter is devoted to selected bio-inspired methods and their application on simple model synthesis from

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