Chaotic sequences to improve the performance of evolutionary algorithms

Caponetto, Riccardo; Fortuna, Luigi; Fazzino, Stefano; Xibilia, Maria Gabriella

doi:10.1109/tevc.2003.810069

Cited by 460 publications

(205 citation statements)

References 16 publications

Supporting

Mentioning

200

Contrasting

Unclassified

Order By: Relevance

“…Segundo, el desarrollo de los algoritmos de optimización de caos basados en la generación de secuencias caóticas en lugar de la generación de números aleatorios. Y tercero, la hibridización de algoritmos de optimización, incluyendo las técnicas basadas en gradientes [15,17,18], algoritmos genéticos [23,24], métodos de punto interior [25], recocido simulado [26], la optimización de enjambre de partícula [27,28], búsqueda tabú [29], búsqueda por coordenadas [30,31] y algoritmos evolutivos [17], entre otros.…”

Section: Caos Y Algoritmos Deunclassified

“…Recientemente, la teoría de sistemas caóticos ha sido aprovechada para desarrollar nuevas metodologías de optimización [12][13][14]; una de sus aplicaciones más importantes es el uso de secuencias numéricas generadas a partir de un mapa caótico, en vez de generadores de números seudo-aleatorios [12,13,15,16], así como también, la hibridación de algoritmos existentes [16][17][18][19].…”

Section: Introductionunclassified

See 1 more Smart Citation

Búsqueda aleatoria repetitiva basada en caos

García

Restrepo

Velásquez

2013

rev.ing.univ.Medellin

View full text Add to dashboard Cite

RESUMENEn este artículo se presenta una modificación del algoritmo de búsqueda aleatoria repetitiva. En esta propuesta se propone cambiar los parámetros fijos, que son ingresados por el usuario, por valores deterministas usando un mapa caótico. El algoritmo propuesto se usó para optimizar 4 funciones de prueba bien conocidas en 10, 20 y 30 dimensiones. Para todas las funciones de prueba, el algoritmo propuesto converge a mejores puntos que los puntos óptimos obtenidos usando la versión tradicional en la que el usuario fija los parámetros. Los resultados obtenidos motivan a continuar con el desarrollo y pruebas del algoritmo propuesto, para un mayor conjunto de funciones de prueba y comparar con otros algoritmos heurísticos establecidos.Palabras clave: algoritmos; caos; métodos de minimización; investigación de operaciones; métodos de optimización; métodos de búsqueda.

show abstract

Section: Caos Y Algoritmos Deunclassified

Section: Introductionunclassified

Búsqueda aleatoria repetitiva basada en caos

García

Restrepo

Velásquez

2013

rev.ing.univ.Medellin

View full text Add to dashboard Cite

show abstract

“…Many works have been reported in literature in order to map the system chaos and noise within the working dimensions of EAs (Nissen and Propach, 1998;Rana et al, 1996;Caponetto et al, 2003). Caponetto et al (2003) introduced chaotic sequences instead of random sequences during all the phases of random evolution process.…”

Section: Binary Stringmentioning

confidence: 99%

“…Caponetto et al (2003) introduced chaotic sequences instead of random sequences during all the phases of random evolution process. The approach was based on the spread spectrum characteristic of chaotic sequences.…”

Section: Binary Stringmentioning

confidence: 99%

Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

Dashora

Kumar

Shukla³

et al. 2008

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

This paper characterizes general optimization problems into four categories based on the solution representation schemes, as they have been the key to the design of various evolutionary algorithms (EAs). Four EAs have been designed for different formulations with the aim of utilizing similar and generalized strategies for all of them. Several modifications to the existing EAs have been proposed and studied. First, a new tradeoff function-based mutation has been proposed that takes advantages of Cauchy, Gaussian, random as well as chaotic mutations. In addition, a generalized learning rule has also been proposed to ensure more thorough and explorative search. A theoretical analysis has been performed to establish the convergence of the learning rule. A theoretical study has also been performed in order to investigate the various aspects of the search strategy employed by the new tradeoff-based mutations. A more logical parameter tuning has been done by introducing the concept of orthogonal arrays in the EA experimentation. The use of noise-based tuning ensures the robust parameter tuning that enables the EAs to perform remarkably well in the further experimentations. The performance of the proposed EAs has been analyzed for different problems of varying complexities. The results prove the supremacy of the proposed EAs over other well-established strategies given in the literature. r

show abstract

“…[2]. A chaotic approach generally uses the chaotic map in the place of a pseudo random number generator [3] …”

Section: Introductionmentioning

confidence: 99%

On the adaptivity and complexity embedded into differential evolution

Šenkeřík

Pluháček

Zelinka

et al. 2016

AIP Conference Proceedings

View full text Add to dashboard Cite

Abstract. This research deals with the comparison of the two modern approaches for evolutionary algorithms, which are the adaptivity and complex chaotic dynamics. This paper aims on the investigations on the chaos-driven Differential Evolution (DE) concept. This paper is aimed at the embedding of discrete dissipative chaotic systems in the form of chaotic pseudo random number generators for the DE and comparing the influence to the performance with the state of the art adaptive representative jDE. This research is focused mainly on the possible disadvantages and advantages of both compared approaches. Repeated simulations for Lozi map driving chaotic systems were performed on the simple benchmark functions set, which are more close to the real optimization problems. Obtained results are compared with the canonical not-chaotic and not adaptive DE. Results show that with used simple test functions, the performance of ChaosDE is better in the most cases than jDE and Canonical DE, furthermore due to the unique sequencing in CPRNG given by the hidden chaotic dynamics, thus better and faster selection of unique individuals from population, ChaosDE is faster.

show abstract

Chaotic sequences to improve the performance of evolutionary algorithms

Cited by 460 publications

References 16 publications

Búsqueda aleatoria repetitiva basada en caos

Búsqueda aleatoria repetitiva basada en caos

Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

On the adaptivity and complexity embedded into differential evolution

Contact Info

Product

Resources

About