scholarly journals An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1866
Author(s):  
Kei Ohnishi ◽  
Kouta Hamano ◽  
Mario Koeppen
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Search Strategy ◽  
Empirical Investigation ◽  
Optimization Problems ◽  
Machine Learning Technique ◽  
Model Based ◽  
Fitness Value ◽  
Learning Technique ◽  
The Difference

Recently, evolutionary algorithms that can efficiently solve decomposable binary optimization problems have been developed. They are so-called model-based evolutionary algorithms, which build a model for generating solution candidates by applying a machine learning technique to a population. Their central procedure is linkage detection that reveals a problem structure, that is, how the entire problem consists of sub-problems. However, the model-based evolutionary algorithms have been shown to be ineffective for problems that do not have relevant structures or those whose structures are hard to identify. Therefore, evolutionary algorithms that can solve both types of problems quickly, reliably, and accurately are required. The objective of the paper is to investigate whether the evolutionary algorithm evolving developmental timings (EDT) that we previously proposed can be the desired one. The EDT makes some variables values more quickly converge than the remains for any problems, and then, decides values of the remains to obtain a higher fitness value under the fixation of the variables values. In addition, factors to decide which variable values converge more quickly, that is, developmental timings are evolution targets. Simulation results reveal that the EDT has worse performance than the linkage tree genetic algorithm (LTGA), which is one of the state-of-the-art model-based evolutionary algorithms, for decomposable problems and also that the difference in the performance between them becomes smaller for problems with overlaps among linkages and also that the EDT has better performance than the LTGA for problems whose structures are hard to identify. Those results suggest that an appropriate search strategy is different between decomposable problems and those hard to decompose.

A many-objective evolutionary algorithm based on vector angle distance scaling

2021 ◽  
Vol 40 (5) ◽  
pp. 10285-10306
Author(s):  
Xin Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Optimization Problems ◽  
Adaptive Strategy ◽  
Great Success ◽  
Multi Objective ◽  
Environmental Selection ◽  
Fitness Value ◽  
Vector Angle

In the past two decades, multi-objective evolutionary algorithms (MOEAs) have achieved great success in solving two or three multi-objective optimization problems. As pointed out in some recent studies, however, MOEAs face many difficulties when dealing with many-objective optimization problems(MaOPs) on account of the loss of the selection pressure of the non-dominant candidate solutions toward the Pareto front and the ineffective design of the diversity maintenance mechanism. This paper proposes a many-objective evolutionary algorithm based on vector guidance. In this algorithm, the value of vector angle distance scaling(VADS) is applied to balance convergence and diversity in environmental selection. In addition, tournament selection based on the aggregate fitness value of VADS is applied to generate a high quality offspring population. Besides, we adopt an adaptive strategy to adjust the reference vector dynamically according to the scales of the objective functions. Finally, the performance of the proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms on 52 instances of 13 MaOPs with diverse characteristics. Experimental results show that the proposed algorithm performs competitively when dealing many-objective with different types of Pareto front.

scholarly journals Multiple infill criterion-assisted hybrid evolutionary optimization for medium-dimensional computationally expensive problems

2021 ◽  
Author(s):  
Shufen Qin ◽  
Chan Li ◽  
Chaoli Sun ◽  
Guochen Zhang ◽  
Xiaobo Li
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Global Search ◽  
Model Management ◽  
The Other ◽  
Local Searches ◽  
Fitness Value ◽  
Expensive Problems ◽  
Computationally Expensive

AbstractSurrogate-assisted evolutionary algorithms have been paid more and more attention to solve computationally expensive problems. However, model management still plays a significant importance in searching for the optimal solution. In this paper, a new method is proposed to measure the approximation uncertainty, in which the differences between the solution and its neighbour samples in the decision space, and the ruggedness of the objective space in its neighborhood are both considered. The proposed approximation uncertainty will be utilized in the surrogate-assisted global search to find a solution for exact objective evaluation to improve the exploration capability of the global search. On the other hand, the approximated fitness value is adopted as the infill criterion for the surrogate-assisted local search, which is utilized to improve the exploitation capability to find a solution close to the real optimal solution as much as possible. The surrogate-assisted global and local searches are conducted in sequence at each generation to balance the exploration and exploitation capabilities of the method. The performance of the proposed method is evaluated on seven benchmark problems with 10, 20, 30 and 50 dimensions, and one real-world application with 30 and 50 dimensions. The experimental results show that the proposed method is efficient for solving the low- and medium-dimensional expensive optimization problems by compared to the other six state-of-the-art surrogate-assisted evolutionary algorithms.

scholarly journals Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems

2021 ◽  
Vol 6 (4 (114)) ◽  
pp. 6-14
Author(s):  
Maan Afathi
Keyword(s):  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Fuzzy Logic Control ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Hybrid Evolutionary Algorithm ◽  
Logic Control

The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency

A Rigorous Complexity Analysis of the (1 + 1) Evolutionary Algorithm for Separable Functions with Boolean Inputs

1998 ◽  
Vol 6 (2) ◽  
pp. 185-196 ◽  
Author(s):  
Stefan Droste ◽  
Thomas Jansen ◽  
Ingo Wegener
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Randomized Algorithms ◽  
Complexity Analysis ◽  
Optimization Problems ◽  
Mutation Rates ◽  
Crossover Operator ◽  
Separable Functions ◽  
Run Time

Evolutionary algorithms (EAs) are heuristic randomized algorithms which, by many impressive experiments, have been proven to behave quite well for optimization problems of various kinds. In this paper a rigorous theoretical complexity analysis of the (1 + 1) evolutionary algorithm for separable functions with Boolean inputs is given. Different mutation rates are compared, and the use of the crossover operator is investigated. The main contribution is not the result that the expected run time of the (1 + 1) evolutionary algorithm is Θ(n ln n) for separable functions with n variables but the methods by which this result can be proven rigorously.

Composite Evolutionary Algorithm for Constrained Optimization

2012 ◽  
Vol 220-223 ◽  
pp. 2846-2851
Author(s):  
Si Lian Xie ◽  
Tie Bin Wu ◽  
Shui Ping Wu ◽  
Yun Lian Liu
Keyword(s):  
Evolutionary Algorithms ◽  
Constrained Optimization ◽  
Evolutionary Algorithm ◽  
Selection Criteria ◽  
Optimization Problems ◽  
Experimental Results ◽  
Constrained Optimization Problems ◽  
Mutation Operators ◽  
Single Operator ◽  
Algorithm Framework

Evolutionary algorithms are amongst the best known methods of solving difficult constrained optimization problems, for which traditional methods are not applicable. Due to the variability of characteristics in different constrained optimization problems, no single evolutionary with single operator performs consistently over a range of problems. We introduce an algorithm framework that uses multiple search operators in each generation. A composite evolutionary algorithm is proposed in this paper and combined feasibility rule to solve constrained optimization problems. The proposed evolutionary algorithm combines three crossover operators with two mutation operators. The selection criteria based on feasibility of individual is used to deal with the constraints. The proposed method is tested on five well-known benchmark constrained optimization problems, and the experimental results show that it is effective and robust

The Efficiency of Interactive Differential Evolution in Creation of Sound Contents

2013 ◽  
Vol 1 (2) ◽  
pp. 16-27 ◽  
Author(s):  
Makoto Fukumoto ◽  
Ryota Yamamoto ◽  
Shintaro Ogawa
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Evolutionary Computation ◽  
Warning Sign ◽  
Interactive Evolutionary Computation ◽  
Interactive Genetic Algorithm ◽  
Fitness Value ◽  
The Creation ◽  
The Difference

Interactive Evolutionary Computation (IEC) is known as an effective method to create media contents suited to user’s preference and objectives. As one of the methods, we have applied Differential Evolution (DE) as evolutionary algorithm in IEC. This study investigated the efficacy of Interactive Differential Evolution (IDE) in comparison with Interactive Genetic Algorithm (IGA). Two listening experiments were conducted to investigate the efficacy: experiment 1 as a creating experiment with IDE and IGA, experiment 2 as a re-evaluating experiment. Target of the creation was warning sign sounds. Eighteen subjects participated in both of the experiments. The result of the experiment 1 showed that IDE overcame IGA, and significant increase of fitness was only observed in IDE. The result of the experiment 2, higher fitness value was observed in IDE, however, the difference between the two conditions was not significant. Parts of the results showed a possibility of IDE to create media contents.

Understanding the Course and State of Evolutionary Optimizations Using Visualization: Ten Years of Industry Experience with Evolutionary Algorithms

2006 ◽  
Vol 12 (2) ◽  
pp. 217-227 ◽  
Author(s):  
Hartmut Pohlheim
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
The Other ◽  
Simple Manner ◽  
Extra Information ◽  
Real World Applications ◽  
The Difference ◽  
Visualization Techniques ◽  
Industry Experience ◽  
Insight Into

Evolutionary algorithms (EAs) are widely employed to solve a broad range of optimization problems. Even though they work in an algorithmically simple manner, it is not always easy to understand what is going on during a particular optimization run. It is especially desirable to gain further insight into the state and course of the algorithm if the optimization does not yield the expected results or if we are not sure whether the result achieved is really the best result possible. During an optimization run an EA produces a vast amount of data. The extraction of useful information is a nontrivial task. In this article, we review visualization methods used to extract this useful information. We also demonstrate the application of visualization techniques and explain how they help us to understand the course and state of the EA. This extra information gained by the use of visualization techniques is often the difference between a good result and a very good result. In complex real-world applications, merely achieving a good result often means that the approach has failed. On the other hand, a success means large gains in productivity or safety or a decrease in costs.

scholarly journals ETEA: A Euclidean Minimum Spanning Tree-Based Evolutionary Algorithm for Multi-Objective Optimization

2014 ◽  
Vol 22 (2) ◽  
pp. 189-230 ◽  
Author(s):  
Miqing Li ◽  
Shengxiang Yang ◽  
Jinhua Zheng ◽  
Xiaohui Liu
Keyword(s):  
Evolutionary Algorithm ◽  
Spanning Tree ◽  
Euclidean Distance ◽  
Minimum Spanning Tree ◽  
Optimization Problems ◽  
Multi Objective Optimization ◽  
Position Information ◽  
Multi Objective ◽  
Environmental Selection ◽  
Fitness Value

The Euclidean minimum spanning tree (EMST), widely used in a variety of domains, is a minimum spanning tree of a set of points in space where the edge weight between each pair of points is their Euclidean distance. Since the generation of an EMST is entirely determined by the Euclidean distance between solutions (points), the properties of EMSTs have a close relation with the distribution and position information of solutions. This paper explores the properties of EMSTs and proposes an EMST-based evolutionary algorithm (ETEA) to solve multi-objective optimization problems (MOPs). Unlike most EMO algorithms that focus on the Pareto dominance relation, the proposed algorithm mainly considers distance-based measures to evaluate and compare individuals during the evolutionary search. Specifically, in ETEA, four strategies are introduced: (1) An EMST-based crowding distance (ETCD) is presented to estimate the density of individuals in the population; (2) A distance comparison approach incorporating ETCD is used to assign the fitness value for individuals; (3) A fitness adjustment technique is designed to avoid the partial overcrowding in environmental selection; (4) Three diversity indicators—the minimum edge, degree, and ETCD—with regard to EMSTs are applied to determine the survival of individuals in archive truncation. From a series of extensive experiments on 32 test instances with different characteristics, ETEA is found to be competitive against five state-of-the-art algorithms and its predecessor in providing a good balance among convergence, uniformity, and spread.

A Multifractal-Guided Multilevel Surrogate Model-Based Evolutionary Algorithm for Expensive Multiobjective Problems

2017 ◽  
Vol 26 (07) ◽  
pp. 1750109 ◽  
Author(s):  
Dongmei Zhang ◽  
Jianping Liao ◽  
Xiaohui Huang ◽  
Jiaqi Jin
Keyword(s):  
Evolutionary Algorithm ◽  
Surrogate Model ◽  
Optimization Problems ◽  
Standard Test ◽  
Sample Distribution ◽  
Model Based ◽  
Multiobjective Problems ◽  
Global Trend ◽  
Computational Resources ◽  
Model Transition

In applied engineering, there are tremendous optimization problems which are multiobjective problems. Meanwhile, a number of them require large amount of time to evaluate their expensive cost function during optimization procedures. This kind of problems can be either financially expensive due to significant computational resources being required or time expensive due to numerous computational complexity. Aiming to this kind of problems, this paper proposed a multilevel surrogate model-based evolutionary algorithm. The proposed method employs DACE modeling method at the beginning to obtain a global trend in the decision domain. When more and more samples are involved and the sample distribution presents a trend or a manifold, the SVR model is utilized as a second-level surrogate model to achieve a better local search. The model transition is determined by the multifractal analysis on the solution set. Experimental results on ZDT and DTLZ standard test cases demonstrate that the time for EGO modeling can be reduced, and the accuracy can be better balanced by comparing to existing SVR and EGO methods.

Parallel Evolutionary Algorithm for Computationally Expensive Single Criteria Design Optimization

2014 ◽  
Vol 555 ◽  
pp. 586-592
Author(s):  
Stanisław Krenich
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Design Optimization ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Computation Method ◽  
Mechanism Optimization ◽  
Parallel Evolutionary Algorithms ◽  
Parallel Evolutionary Algorithm ◽  
Method Analysis

The paper presents an approach to design optimization using parallel evolutionary algorithms. The only use of a simple evolutionary algorithm in order to generate the optimal solution for complex problems can be ineffective due to long calculation time. Thus a tournament evolutionary algorithm (EA) and a parallel computation method are proposed and used. The proposed EA does not require an analysis of the optimization model for each potential solution from evolutionary populations. The second element of the method consists in parallel running of evolutionary algorithms using multi-threads approach. The experiments were carried out for many different single design optimization problems and two of them are presented in the paper. The first problem considers a task of robot gripper mechanism optimization and the second one deals with the optimization of a shaft based on Finite Element Method analysis. From the generated results it is clear that proposed approach is a very effective tool for solving fairly complicated tasks considering both the accuracy and the time of calculation.

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