Hybridizing genetic algorithm with cross entropy for solving continuous functions

Pedro Lopez-Garcia; Enrique Onieva; Eneko Osaba; Antonio D. Masegosa; Asier Perallos

doi:10.1145/2739482.2764881

Hybridizing genetic algorithm with cross entropy for solving continuous functions

Pedro Lopez-Garcia
, Enrique Onieva
, Eneko Osaba
, Antonio D. Masegosa
, Asier Perallos

University of Deusto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

In this paper, a metaheuristic that combines a Genetic Algorithm and a Cross Entropy Algorithm is presented. The aim of this work is to achieve a synergy between the capabilities of the algorithms using different population sizes in order to obtain the closest value to the optimal of the function. The proposal is applied to 12 benchmark functions with different characteristics, using different configurations.

Original language	English
Title of host publication	GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference
Editors	Sara Silva
Publisher	Association for Computing Machinery, Inc
Pages	763-764
Number of pages	2
ISBN (Electronic)	9781450334884
DOIs	https://doi.org/10.1145/2739482.2764881
Publication status	Published - 11 Jul 2015
Externally published	Yes
Event	17th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain Duration: 11 Jul 2015 → 15 Jul 2015

Publication series

Name	GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference

Conference

Conference	17th Genetic and Evolutionary Computation Conference, GECCO 2015
Country/Territory	Spain
City	Madrid
Period	11/07/15 → 15/07/15

Keywords

Algorithms
Cross entropy
Experimentation
Genetic algorithm
Hybridization technique
Meta-heuristic
Optimization problem
Real-world problem

Access to Document

10.1145/2739482.2764881

Cite this

Lopez-Garcia, P., Onieva, E., Osaba, E., Masegosa, A. D., & Perallos, A. (2015). Hybridizing genetic algorithm with cross entropy for solving continuous functions. In S. Silva (Ed.), GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference (pp. 763-764). (GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/2739482.2764881

Lopez-Garcia, Pedro ; Onieva, Enrique ; Osaba, Eneko et al. / Hybridizing genetic algorithm with cross entropy for solving continuous functions. GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. editor / Sara Silva. Association for Computing Machinery, Inc, 2015. pp. 763-764 (GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference).

@inproceedings{1377db60614b4cac804a05b41acd5e5d,

title = "Hybridizing genetic algorithm with cross entropy for solving continuous functions",

abstract = "In this paper, a metaheuristic that combines a Genetic Algorithm and a Cross Entropy Algorithm is presented. The aim of this work is to achieve a synergy between the capabilities of the algorithms using different population sizes in order to obtain the closest value to the optimal of the function. The proposal is applied to 12 benchmark functions with different characteristics, using different configurations.",

keywords = "Algorithms, Cross entropy, Experimentation, Genetic algorithm, Hybridization technique, Meta-heuristic, Optimization problem, Real-world problem",

author = "Pedro Lopez-Garcia and Enrique Onieva and Eneko Osaba and Masegosa, \{Antonio D.\} and Asier Perallos",

year = "2015",

month = jul,

day = "11",

doi = "10.1145/2739482.2764881",

language = "English",

series = "GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference",

publisher = "Association for Computing Machinery, Inc",

pages = "763--764",

editor = "Sara Silva",

booktitle = "GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference",

note = "17th Genetic and Evolutionary Computation Conference, GECCO 2015 ; Conference date: 11-07-2015 Through 15-07-2015",

}

Lopez-Garcia, P, Onieva, E, Osaba, E, Masegosa, AD & Perallos, A 2015, Hybridizing genetic algorithm with cross entropy for solving continuous functions. in S Silva (ed.), GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Inc, pp. 763-764, 17th Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, 11/07/15. https://doi.org/10.1145/2739482.2764881

Hybridizing genetic algorithm with cross entropy for solving continuous functions. / Lopez-Garcia, Pedro; Onieva, Enrique; Osaba, Eneko et al.
GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. ed. / Sara Silva. Association for Computing Machinery, Inc, 2015. p. 763-764 (GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Hybridizing genetic algorithm with cross entropy for solving continuous functions

AU - Lopez-Garcia, Pedro

AU - Onieva, Enrique

AU - Osaba, Eneko

AU - Masegosa, Antonio D.

AU - Perallos, Asier

PY - 2015/7/11

Y1 - 2015/7/11

N2 - In this paper, a metaheuristic that combines a Genetic Algorithm and a Cross Entropy Algorithm is presented. The aim of this work is to achieve a synergy between the capabilities of the algorithms using different population sizes in order to obtain the closest value to the optimal of the function. The proposal is applied to 12 benchmark functions with different characteristics, using different configurations.

AB - In this paper, a metaheuristic that combines a Genetic Algorithm and a Cross Entropy Algorithm is presented. The aim of this work is to achieve a synergy between the capabilities of the algorithms using different population sizes in order to obtain the closest value to the optimal of the function. The proposal is applied to 12 benchmark functions with different characteristics, using different configurations.

KW - Algorithms

KW - Cross entropy

KW - Experimentation

KW - Genetic algorithm

KW - Hybridization technique

KW - Meta-heuristic

KW - Optimization problem

KW - Real-world problem

UR - https://www.scopus.com/pages/publications/84959338765

U2 - 10.1145/2739482.2764881

DO - 10.1145/2739482.2764881

M3 - Conference contribution

AN - SCOPUS:84959338765

T3 - GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference

SP - 763

EP - 764

BT - GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference

A2 - Silva, Sara

PB - Association for Computing Machinery, Inc

T2 - 17th Genetic and Evolutionary Computation Conference, GECCO 2015

Y2 - 11 July 2015 through 15 July 2015

ER -

Lopez-Garcia P, Onieva E, Osaba E, Masegosa AD, Perallos A. Hybridizing genetic algorithm with cross entropy for solving continuous functions. In Silva S, editor, GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc. 2015. p. 763-764. (GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference). doi: 10.1145/2739482.2764881

Hybridizing genetic algorithm with cross entropy for solving continuous functions

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