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Memetic Computing
Published in: Memetic Computing 3/2019

06-04-2019 | Regular Research Paper

OMNIREP: originating meaning by coevolving encodings and representations

Authors: Moshe Sipper, Jason H. Moore

Published in: Memetic Computing | Issue 3/2019

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Abstract

A major effort in the practice of evolutionary computation goes into deciding how to represent individuals in the evolving population. This task is actually composed of two subtasks: defining a data structure that is the representation and defining the encoding that enables to interpret the representation. In this paper we employ a coevolutionary algorithm—dubbed OMNIREP—to discover both a representation and an encoding that solve a particular problem of interest. We describe four experiments that provide a proof-of-concept of OMNIREP’s essential merit. We think that the proposed methodology holds potential as a problem solver and also as an exploratory medium when scouting for good representations.
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Footnotes
2
The OMNIREP code is available at https://​github.​com/​EpistasisLab/​.
 
3
Some parameters may seem arbitrary but our recent findings provide some justification for this [25].
 
5
Of course, some representations, such as trees in genetic programming, are inherently variable-length. Herein, we simply refer to the literature on “variable-length genomes”.
 
Literature
1.
Angeline PJ, Pollack JB (1994) Coevolving high-level representations. In: Langton CG (ed) Artificial life III, vol XVII of SFI studies in the sciences of complexity. Addison-Wesley, Santa Fe, pp 55–71
2.
Azad RMA, Ryan C (2006) An examination of simultaneous evolution of grammars and solutions. In: Yu T, Riolo R, Worzel B (eds) Genetic programming theory and practice III. Springer, Boston, pp 141–158CrossRef
3.
Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic programming—an introduction; on the automatic evolution of computer programs and its applications. Morgan Kaufmann, San FranciscoMATH
4.
Bentley P, Kumar S (1999) Three ways to grow designs: a comparison of embryogenies for an evolutionary design problem. In: Proceedings of the 1st annual conference on genetic and evolutionary computation-GECCO’99, vol 1. Morgan Kaufmann Publishers Inc., San Francisco, pp 35–43
5.
Caraffini F, Neri F, Picinali L (2014) An analysis on separability for memetic computing automatic design. Inf Sci 265:1–22MathSciNetCrossRef
6.
Correia J, Ciesielski V, Liapis A (2017) Proceedings of computational intelligence in music, sound, art and design: 6th international conference. Springer, BerlinCrossRef
7.
8.
Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129MathSciNetMATH
9.
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., BostonMATH
10.
Goldberg DE, Korb B, Deb K (1989) Messy genetic algorithms: motivation, analysis, and first results. Complex Syst 3:493–530MathSciNetMATH
11.
Gruau F, Whitley D, Pyeatt L (1996) A comparison between cellular encoding and direct encoding for genetic neural networks. In: Proceedings of the 1st annual conference on genetic programming. MIT Press, Cambridge, pp 81–89
12.
Hart WE, Kammeyer TE, Belew RK (1995) The role of development in genetic algorithms. In: Whitley LD, Vose MD (eds) Foundations of genetic algorithms, vol 3. Elsevier, Amsterdam, pp 315–332
13.
Hornby GS, Pollack JB (2002) Creating high-level components with a generative representation for body-brain evolution. Artif Life 8(3):223–246CrossRef
14.
Iacca G, Caraffini F, Neri F (2014) Multi-strategy coevolving aging particle optimization. Int J Neural Syst 24(01):1450008CrossRef
15.
Iacca G, Neri F, Mininno E, Ong Y-S, Lim M-H (2012) Ockham’s razor in memetic computing: three stage optimal memetic exploration. Inf Sci 188:17–43MathSciNetCrossRef
16.
Koza JR (2003) Genetic programming IV: routine human-competitive machine intelligence. Kluwer Academic Publishers, NorwellMATH
17.
Lee CY, Antonsson EK (2000) Variable length genomes for evolutionary algorithms. In: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, San Francisco
18.
Mitchell M (1998) An introduction to genetic algorithms. MIT press, CambridgeMATH
19.
Neri F, Cotta C (2012) Memetic algorithms and memetic computing optimization: a literature review. Swarm Evol Comput 2:1–14CrossRef
20.
Neri F, Cotta C, Moscato P (2012) Handbook of memetic algorithms, vol 379. Springer, BerlinCrossRef
21.
Nicolau M, Ryan C (2002) LINKGAUGE: tackling hard deceptive problems with a new linkage learning genetic algorithm. In: Proceedings of the 4th annual conference on genetic and evolutionary computation. Morgan Kaufmann Publishers Inc., San Francisco, pp 488–494
22.
Orlov M, Sipper M (2011) Flight of the FINCH through the Java wilderness. IEEE Trans Evol Comput 15(2):166–182CrossRef
23.
Pena-Reyes CA, Sipper M (2001) Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling. IEEE Trans Fuzzy Syst 9(5):727–737CrossRef
24.
Ryan C, Collins JJ, O’Neill M (1998) Grammatical evolution: evolving programs for an arbitrary language. In: Proceedings genetic programming, first European workshop, EuroGP’98. Paris, pp 83–96
25.
Sipper M, Fu W, Ahuja K, Moore JH (2018) Investigating the parameter space of evolutionary algorithms. BioData Min 11(2):1–14
26.
Stanley KO, D’Ambrosio DB, Gauci J (2009) A hypercube-based encoding for evolving large-scale neural networks. Artif Life 15(2):185–212CrossRef
27.
Stanley KO, Miikkulainen R (2003) A taxonomy for artificial embryogeny. Artif Life 9(2):93–130CrossRef
28.
Zaritsky A, Sipper M (2004) The preservation of favored building blocks in the struggle for fitness: the puzzle algorithm. IEEE Trans Evol Comput 8(5):443–455CrossRef
29.
Zhang G, Rong H, Neri F, Pérez-Jiménez MJ (2014) An optimization spiking neural p system for approximately solving combinatorial optimization problems. Int J Neural Syst 24(05):1440006CrossRef
Metadata
Title
OMNIREP: originating meaning by coevolving encodings and representations
Authors
Moshe Sipper
Jason H. Moore
Publication date
06-04-2019
Publisher
Springer Berlin Heidelberg
Published in
Memetic Computing / Issue 3/2019
Print ISSN: 1865-9284
Electronic ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-019-00285-2

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