Simple Explanation of the No-Free-Lunch Theorem and Its Implications

Ho, Yu-Chi; Pepyne, D.L.

doi:10.1023/a:1021251113462

Cited by 456 publications

(232 citation statements)

References 7 publications

Supporting

Mentioning

220

Contrasting

Unclassified

Order By: Relevance

“…These simulation results do not indicate that BOA/ABC is "better" than BOA and ABC. As this common statement would be an unjustified remark, particularly in view of the no free lunch theorem [35]. However, these results illustrate that BOA/ABC demonstrates superior performance than BOA and ABC on the particular benchmark functions used in this study.…”

Section: Functionmentioning

confidence: 65%

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

Arora¹,

Singh²

2017

IJIMAI

View full text Add to dashboard Cite

-In this paper, a new hybrid optimization algorithm which combines the standard Butterfly Optimization Algorithm (BOA) with Artificial Bee Colony (ABC) algorithm is proposed. The proposed algorithm used the advantages of both the algorithms in order to balance the trade-off between exploration and exploitation. Experiments have been conducted on the proposed algorithm using ten benchmark problems having a broad range of dimensions and diverse complexities. The simulation results demonstrate that the convergence speed and accuracy of the proposed algorithm in finding optimal solutions is significantly better than BOA and ABC.

show abstract

Section: Functionmentioning

confidence: 65%

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

Arora¹,

Singh²

2017

IJIMAI

View full text Add to dashboard Cite

show abstract

“…This may well be different in other global optimization problems. Hence, even for LHP-EA, by construction there still is no free lunch [46,47].…”

Section: Resultsmentioning

confidence: 99%

Improved Cluster Structure Optimization: Hybridizing Evolutionary Algorithms with Local Heat Pulses

Dieterich

Hartke

2017

Inorganics

View full text Add to dashboard Cite

Abstract:Cluster structure optimization (CSO) refers to finding the globally minimal cluster structure with respect to a specific model and quality criterion, and is a computationally extraordinarily hard problem. Here we report a successful hybridization of evolutionary algorithms (EAs) with local heat pulses (LHPs). We describe the algorithm's implementation and assess its performance with hard benchmark CSO cases. EA-LHP showed superior performance compared to regular EAs. Additionally, the EA-LHP hybrid is an unbiased, general CSO algorithm requiring no system-specific solution knowledge. These are compelling arguments for a wider future use of EA-LHP in CSO.

show abstract

“…Genetic Algorithms and Simulated Annealing) without exploiting as much domain knowledge as possible. Alternatively, the theorem establishes that a general-purpose universal optimization strategy is not possible, and the only way one strategy can outperform another is when it is specially adapted to the problem under consideration (Ho and Pepyne, 2002).…”

Section: Summary Of Abbreviationsmentioning

confidence: 99%

The development and application of metaheuristics for problems in graph theory: a computational study

Consoli¹

2011

4OR-Q J Oper Res

View full text Add to dashboard Cite

Simple Explanation of the No-Free-Lunch Theorem and Its Implications

Cited by 456 publications

References 7 publications

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

Improved Cluster Structure Optimization: Hybridizing Evolutionary Algorithms with Local Heat Pulses

The development and application of metaheuristics for problems in graph theory: a computational study

Contact Info

Product

Resources

About