Selection Operators Based on Maximin Fitness Function for Multi-Objective Evolutionary Algorithms

2013 ◽  
pp. 215-229 ◽  
Author(s):  
Adriana Menchaca-Mendez ◽  
Carlos A. Coello Coello
Keyword(s):  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Multi Objective ◽  
Selection Operators

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

scholarly journals What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains?

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 232 ◽  
Author(s):  
Roberto Ugolotti ◽  
Laura Sani ◽  
Stefano Cagnoni
Keyword(s):  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Target Function ◽  
Optimization Methods ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Continuous Domains ◽  
Level Method ◽  
Meta Optimization ◽  
Function Time

Properly configuring Evolutionary Algorithms (EAs) is a challenging task made difficult by many different details that affect EAs’ performance, such as the properties of the fitness function, time and computational constraints, and many others. EAs’ meta-optimization methods, in which a metaheuristic is used to tune the parameters of another (lower-level) metaheuristic which optimizes a given target function, most often rely on the optimization of a single property of the lower-level method. In this paper, we show that by using a multi-objective genetic algorithm to tune an EA, it is possible not only to find good parameter sets considering more objectives at the same time but also to derive generalizable results which can provide guidelines for designing EA-based applications. In particular, we present a general framework for multi-objective meta-optimization, to show that “going multi-objective” allows one to generate configurations that, besides optimally fitting an EA to a given problem, also perform well on previously unseen ones.

Optimization of Production Equipment Layout Based on Fuzzy Decision and Evolutionary Algorithm

2019 ◽  
Vol 11 (3) ◽  
pp. 13-29
Author(s):  
Wenfang Chen
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Model ◽  
Fitness Function ◽  
Production Equipment ◽  
Production Costs ◽  
Practical Case ◽  
Fuzzy Decision ◽  
Pareto Solution ◽  
Total Cost ◽  
Multi Objective

A method of optimizing equipment layout is proposed in production equipment layout of a complex manufacturing system, using fuzzy decisions combined with evolutionary algorithms. First, the optimization model is improved, the total cost is minimized, while the requirements of adjacent equipment and the space utilization are maximized; the material handling cost, the resetting cost, the loss of production costs are considered in the total cost objective. Second, this method takes into account the ambiguity of the satisfaction and priority of users such as cost, utilization and proximity requirements, based on the fuzzy decision theory, the multi-objective optimization model is fuzzified, and the fuzzy fitness function is designed to evaluate the pareto solution set according to the user's priority relation. Based on the characteristics of the solution model, the chromosomal coding method of multi-objective evolutionary algorithms and the genetic operation mode are improved, and the practicability and efficiency of the model is improved. Finally, the effectiveness of the method is proved by the optimization of the practical case.

Multi-Objective Optimization with Controlled Model Assisted Evolution Strategies

2009 ◽  
Vol 17 (4) ◽  
pp. 577-593 ◽  
Author(s):  
Jan Braun ◽  
Johannes Krettek ◽  
Frank Hoffmann ◽  
Torsten Bertram
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Fitness Function ◽  
Pressure Control ◽  
Evolution Strategy ◽  
Model Quality ◽  
Contrast Model ◽  
Loop Optimization ◽  
Multi Objective ◽  
Search Spaces

Evolutionary algorithms perform robust search in complex and high dimensional search spaces, but require a large number of fitness evaluations to approximate optimal solutions. These characteristics limit their potential for hardware in the loop optimization and problems that require extensive simulations and calculations. Evolutionary algorithms do not maintain their knowledge about the fitness function as they only store solutions of the current generation. In contrast, model assisted evolutionary algorithms utilize the information contained in previously evaluated solutions in terms of a data based model. The convergence of the evolutionary algorithm is improved as some selection decisions rely on the model rather than to invoke expensive evaluations of the true fitness function. The novelty of our scheme stems from the preselection of solutions based on an instance based fitness model, in which the selection pressure is adjusted to the quality of model. This so-called λ-control adapts the number of true fitness evaluations to the monitored model quality. Our method extends the previous approaches for model assisted scalar optimization to multi-objective problems by a proper redefinition of model quality and preselection pressure control. The analysis on multi-objective benchmark optimization problems not only confirms the superior convergence of the model assisted evolution strategy in comparison with a multi-objective evolution strategy but also the positive effect of regulated preselection in contrast to merely static preselection.

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