Optimization of multimodal continuous functions using a new crossover for the real-coded genetic algorithms

Tutkun, Nedim

doi:10.1016/j.eswa.2008.10.042

Cited by 21 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this purpose, sixteen test functions were used based on 100 runs. The results are shown in Table 4 F5-F6-F7-F9-F11-F12-F13 GA-PSO Genetic algorithm particle swarm optimization [8] F4-F7-F9 GAWLS Genetic algorithm [32] F1-F3 HAP Hybrid ant particle optimization algorithm [4] F1-F4-F10 ACO-NPU Ant colony optimization [9] All problems MORELA Modified reinforcement learning algorithm (This study) able to solve this function, it produces better functional value than those provided by other compared algorithms, as shown in Table 4. MORELA also produces less average error for all test functions than the other methods considered.…”

Section: Further Comparisons Of Morela With Other Methodsmentioning

confidence: 99%

A Novel Approach Based on Reinforcement Learning for Finding Global Optimum

Ozan¹,

Başkan²,

Haldenbilen³

2017

OJOp

View full text Add to dashboard Cite

A novel approach to optimizing any given mathematical function, called the MOdified REinforcement Learning Algorithm (MORELA), is proposed. Although Reinforcement Learning (RL) is primarily developed for solving Markov decision problems, it can be used with some improvements to optimize mathematical functions. At the core of MORELA, a sub-environment is generated around the best solution found in the feasible solution space and compared with the original environment. Thus, MORELA makes it possible to discover global optimum for a mathematical function because it is sought around the best solution achieved in the previous learning episode using the sub-environment. The performance of MORELA has been tested with the results obtained from other optimization methods described in the literature. Results exposed that MORELA improved the performance of RL and performed better than many of the optimization methods to which it was compared in terms of the robustness measures adopted.

show abstract

Section: Further Comparisons Of Morela With Other Methodsmentioning

confidence: 99%

A Novel Approach Based on Reinforcement Learning for Finding Global Optimum

Ozan¹,

Başkan²,

Haldenbilen³

2017

OJOp

View full text Add to dashboard Cite

show abstract

“…[9]. Recently, there have been several attempts to use floating-point numbers or real encoding to overcome these binary coding problems and to speed up the convergence process [15]. Therefore, we use a real-coded EDA.…”

Section: A Encoding Schemementioning

confidence: 99%

Novel dynamic diversity controlling EDA and its application to automated bilateral negotiation

Gwak

Sim

2010

2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

View full text Add to dashboard Cite

Since optimal strategies ensure that agents negotiate optimally, finding optimal strategies for negotiation agents that have incomplete information is an important and challenging issue. In this study, we use estimation of distribution algorithms (EDAs) to find optimal strategies for a bilateral negotiation with incomplete information between two competitive negotiation agents by coevolving both the agents' strategies. Agents coevolve optimal negotiation strategies for both parties through an evolutionary learning process in which a coevolutionary interaction is performed by directly matching and interacting agents of one population with those of the other population through random pairing. Even though there have been studies on finding negotiation strategies using evolutionary approaches, there are very few works on effectively finding the global optimal solution. Finding both parties' optimal strategies is difficult because simple EDAs suffer from premature convergence and their search capability deteriorates during coevolution. To solve these problems, we r.reviously proposed the dynamic diversity controlling EDA (D C-EDA), which has a novel dynamic diversity controlling capability. However, it suffers from the problem of population reinitialization that leads to a computational overhead. To reduce the computational overhead and to achieve further improvements in terms of solution accuracy, we have proposed an improved D 2 C-EDA (ID 2 C-EDA) by adopting a local neighborhood search. Favorable empirical results support the effectiveness of the proposed ID 2 C EDA.

show abstract

“…Deep and Thakur [9] presented Laplace crossover operator which generates a pair of offspring solution from a pair of parent solutions using Laplace distribution. Tutkun [10] proposed a crossover operator based on Gaussian distribution. Kaelo and Ali [11] suggested integration of different crossover rules in the genetic algorithm and recommended some modifications in applying the crossover rules and localization of searches in their study.…”

Section: Introductionmentioning

confidence: 99%

A Novel Crossover Operator Designed to Exploit Synergies of Two Crossover Operators for Real-Coded Genetic Algorithms

Shashi

Deep

2016

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

In this paper a new crossover operator called the double distribution crossover (DDX) is proposed. The performance of DDX is compared with existing real-coded crossover operator namely Laplace crossover (LX). DDX is used in conjunction with a well-known mutation operator; Power mutation (PM) to obtain a new generational real-coded genetic algorithm called DDX-PM. DDX-PM is compared with the existing LX-PM. The performance of both the genetic algorithms is compared on the basis of success rate, average function evaluation, average error and computational time, and the preeminence of the proposed crossover operator is established.

show abstract

Optimization of multimodal continuous functions using a new crossover for the real-coded genetic algorithms

Cited by 21 publications

References 7 publications

A Novel Approach Based on Reinforcement Learning for Finding Global Optimum

A Novel Approach Based on Reinforcement Learning for Finding Global Optimum

Novel dynamic diversity controlling EDA and its application to automated bilateral negotiation

A Novel Crossover Operator Designed to Exploit Synergies of Two Crossover Operators for Real-Coded Genetic Algorithms

Contact Info

Product

Resources

About