Fuzzy Control of Trade-Off between Exploration and Exploitation Properties of Evolutionary Algorithms

2011 ◽  
pp. 59-66 ◽  
Author(s):  
Adam Slowik
Keyword(s):  
Evolutionary Algorithms ◽  
Fuzzy Control ◽  
Exploration And Exploitation ◽  
Trade Off

scholarly journals Hybridization of multi-objective evolutionary algorithms and fuzzy control for automated construction, tuning, and analysis of neuronal models

2013 ◽  
Vol 14 (Suppl 1) ◽  
pp. P369 ◽  
Author(s):  
Parth Patel ◽  
Myles Johnson-Gray ◽  
Emlyne Forren ◽  
Atish Malik ◽  
Tomasz G Smolinski
Keyword(s):  
Evolutionary Algorithms ◽  
Fuzzy Control ◽  
Multi Objective

Multiobjective Differential Evolution Based on Fuzzy Performance Feedback

2014 ◽  
Vol 5 (4) ◽  
pp. 45-64 ◽  
Author(s):  
Chatkaew Jariyatantiwait ◽  
Gary G. Yen
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Performance Feedback ◽  
Performance Metrics ◽  
Fuzzy Inference ◽  
Optimization Problems ◽  
Evolution Process ◽  
Exploration And Exploitation ◽  
Multiobjective Optimization Problems ◽  
Mutation Strategy

Differential evolution is often regarded as one of the most efficient evolutionary algorithms to tackle multiobjective optimization problems. The key to success of any multiobjective evolutionary algorithms (MOEAs) is maintaining a delicate balance between exploration and exploitation throughout the evolution process. In this paper, the authors propose a Fuzzy-based Multiobjective Differential Evolution (FMDE) that uses performance metrics, specifically hypervolume, spacing, and maximum spread, to measure the state of the evolution process. The authors apply the fuzzy inference rules to these metrics in order to dynamically adjust the associated control parameters of a chosen mutation strategy used in this algorithm. One parameter controls the degree of greedy or exploitation, while another regulates the degree of diversity or exploration of the reproduction phase. Therefore, the authors can appropriately adjust the degree of exploration and exploitation through performance feedback. The performance of FMDE is evaluated on well-known ZDT and DTLZ test suites. The results validate that the proposed algorithm is competitive with respect to chosen state-of-the-art MOEAs.

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