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 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.

Editorial — Recent Progress in Intelligent and Evolutionary Algorithms and their Applications

2015 ◽  
Vol 11 (02) ◽  
pp. 115-120
Author(s):  
Aki-Hiro Sato ◽  
Hiroshi Kawakami ◽  
Toshihiro Hiraoka
Keyword(s):  
Evolutionary Algorithms ◽  
Real World ◽  
Recent Progress ◽  
Optimization Problems ◽  
Asia Pacific ◽  
Topical Issue ◽  
Special Issue ◽  
Mathematical Methods ◽  
Evolutionary Systems ◽  
Real World Applications

This is a topical issue on the 16th Asia–Pacific Symposium on Intelligent and Evolutionary Systems (IES) which was held in Kyoto from December 12–14, 2012. This special issue contains six articles related to evolutionary algorithms that are designed to solve optimization problems, network concepts, mathematical methods and their real world applications.

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.

Modified Differential Evolution for Tension/Compression String Design Problem

2013 ◽  
Vol 705 ◽  
pp. 523-527
Author(s):  
Li Jian ◽  
Cheng Jiu Yin ◽  
Sachio Hirokawa ◽  
Yoshiyuki Tabata
Keyword(s):  
Social Networks ◽  
Evolutionary Algorithms ◽  
Local Search ◽  
Differential Evolution ◽  
Design Problem ◽  
Global Search ◽  
The Other ◽  
High Connectivity ◽  
The Difference

This paper introduces a modified differential evoluiton method to solve the tension/compression string design problem. The modification is derived from mechanisms of social networks. In the proposed method, each individual will be attracted by the knowed best individual following the connectivity between each other. The connectivity is calculated based on the difference of the variables in each vector. The individuals with high connectivity tend to perform local search while those with poor connectivity tend to perform global search instead. The approach was employed for a tension/compression string design problem and by comparisons with the other evolutionary algorithms, the proposed method privided better resutls.

“Evil Witch! I’m Not Scared!”

2019 ◽  
pp. 93-113
Author(s):  
Justin Wigard
Keyword(s):  
Comic Book ◽  
The Other ◽  
Film Adaptation ◽  
Color Palette ◽  
Dominant Discourse ◽  
Visual Semiotics ◽  
Intertextual Connections ◽  
Visualization Techniques ◽  
Insight Into

In this chapter, Justin Wigard bridges a gap in research on Coraline by focusing on the visual semiotics of the narrative across its adaptations, specifically by examining the intertextual connections between McKean's surreal and monochromatic illustrations in Gaiman's novel, David Russell's comic book adaptation grounded both in realism and a muted pastel color palette, and Henry Selick's garishly, brightly colored film adaptation. The dominant discourse surrounding the canonical children's text has keyed in to the novella's psychoanalytic underpinnings, Gothic conventions, and postfeminist ideology while largely leaving out discussion of the visual aspectsofGaiman's novella, namely McKean's illustrations. When examined intertextually, the illustrative styles used to depict the Other Mother give insight into what monstrous traits are emphasized through medium-specific visualization techniques, ultimately revealing the fears that the Other Mother embodies through visual semiotics.

scholarly journals A Constrained Many-objective Optimization Evolutionary Algorithm with Enhanced Mating and Environmental Selections

2021 ◽  
Author(s):  
Fei Ming
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Density Estimation ◽  
Real World ◽  
Selection Pressure ◽  
Optimization Problems ◽  
Variable Number ◽  
Objective Functions ◽  
Considerable Research ◽  
Real World Applications

<div>Unlike the considerable research on solving many objective optimization problems with evolutionary algorithms, there has been much less research on constrained many-objective optimization problems (CMaOPs). Generally, to effectively solve CMaOPs, an algorithm needs to balance feasibility, convergence, and diversity simultaneously. It is essential for handling CMaOPs yet most of the existing research encounters difficulties. This paper proposes a novel constrained many-objective optimization evolutionary algorithm with enhanced mating and environmental selections, namely CMME. The main features are: i) two ranking strategies are proposed and applied in the mating and environmental selections to enrich feasibility and convergence; ii) an individual density estimation is designed, and crowding distance is integrated to promote diversity; and iii) the ?-dominance is used to strengthen the selection pressure on both the convergence and diversity. The synergy of these components can achieve the goal of balancing feasibility, convergence, and diversity for solving CMaOPs. The proposed CMME algorithm is evaluated on 10 CMaOPs with different features and a variable number of objective functions. Experimental results on three benchmark CMOPs and three real-world applications demonstrate that CMME shows superiority or competitiveness over nine related algorithms.</div>

scholarly journals A Constrained Many-objective Optimization Evolutionary Algorithm with Enhanced Mating and Environmental Selections

2021 ◽  
Author(s):  
Fei Ming
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Density Estimation ◽  
Real World ◽  
Selection Pressure ◽  
Optimization Problems ◽  
Variable Number ◽  
Objective Functions ◽  
Considerable Research ◽  
Real World Applications

<div>Unlike the considerable research on solving many objective optimization problems with evolutionary algorithms, there has been much less research on constrained many-objective optimization problems (CMaOPs). Generally, to effectively solve CMaOPs, an algorithm needs to balance feasibility, convergence, and diversity simultaneously. It is essential for handling CMaOPs yet most of the existing research encounters difficulties. This paper proposes a novel constrained many-objective optimization evolutionary algorithm with enhanced mating and environmental selections, namely CMME. The main features are: i) two ranking strategies are proposed and applied in the mating and environmental selections to enrich feasibility and convergence; ii) an individual density estimation is designed, and crowding distance is integrated to promote diversity; and iii) the ?-dominance is used to strengthen the selection pressure on both the convergence and diversity. The synergy of these components can achieve the goal of balancing feasibility, convergence, and diversity for solving CMaOPs. The proposed CMME algorithm is evaluated on 10 CMaOPs with different features and a variable number of objective functions. Experimental results on three benchmark CMOPs and three real-world applications demonstrate that CMME shows superiority or competitiveness over nine related algorithms.</div>

Evolution Algorithms and Biomass Properties Prediction: A Review

2019 ◽  
Author(s):  
Obafemi Olatunji ◽  
Stephen Akinlabi ◽  
Nkosinathi Madushele ◽  
Paul Adedeji ◽  
Samuel Fatoba
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Biomass Energy ◽  
Cumulative Impact ◽  
Future Directions ◽  
Prediction Technique ◽  
Real World Applications ◽  
Different Types ◽  
Evolution Algorithms ◽  
Hybrid Forms

Abstract The complexity of real-world applications of biomass energy has increased substantially due to so many competing factors. There is an ongoing discussion on biomass as a renewable energy source and its cumulative impact on the environment vis-a-vis water competition, environmental pollution and so on. This discussion is coming at a time when evolutionary algorithms and its hybrid forms are gaining traction in several applications. In the last decade, evolution algorithms and its hybrid forms have evolved as a significant optimization and prediction technique due to its flexible characteristics and robust behaviour. It is very efficient means of solving complex global optimization problems. This article presents the state-of-the-art review of different types of evolutionary algorithms, which have been applied in the prediction of major properties of biomass such as elemental compositions and heating values. The governing principles, applications, merits, and challenges associated with this technique are elaborated. The future directions of the research on biomass properties prediction are discussed.

scholarly journals A Decomposition-Based Unified Evolutionary Algorithm for Many-Objective Problems Using Particle Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Anqi Pan ◽  
Hongjun Tian ◽  
Lei Wang ◽  
Qidi Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Particle Swarm ◽  
The Other ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Multiobjective Optimization Problems ◽  
Reconstruction Strategy ◽  
Leader Selection

Evolutionary algorithms have proved to be efficient approaches in pursuing optimum solutions of multiobjective optimization problems with the number of objectives equal to or less than three. However, the searching performance degenerates in high-dimensional objective optimizations. In this paper we propose an algorithm for many-objective optimization with particle swarm optimization as the underlying metaheuristic technique. In the proposed algorithm, the objectives are decomposed and reconstructed using discrete decoupling strategy, and the subgroup procedures are integrated into unified coevolution strategy. The proposed algorithm consists of inner and outer evolutionary processes, together with adaptive factor μ, to maintain convergence and diversity. At the same time, a designed repository reconstruction strategy and improved leader selection techniques of MOPSO are introduced. The comparative experimental results prove that the proposed UMOPSO-D outperforms the other six algorithms, including four common used algorithms and two newly proposed algorithms based on decomposition, especially in high-dimensional targets.

Is 75% of the Sales Promotion Bump Due to Brand Switching? No, Only 33% Is

2003 ◽  
Vol 40 (4) ◽  
pp. 481-491 ◽  
Author(s):  
Harald J. Van Heerde ◽  
Sachin Gupta ◽  
Dick R. Wittink
Keyword(s):  
Sales Promotion ◽  
The Other ◽  
Brand Switching ◽  
Scanner Panel Data ◽  
Demand Effects ◽  
The Difference ◽  
Scanner Panel ◽  
Analytical Expressions ◽  
Insight Into ◽  
Sales Increase

Several researchers have decomposed sales promotion elasticities based on household scanner-panel data. A key result is that the majority of the sales promotion elasticity, approximately 74% on average, is attributed to secondary demand effects (brand switching) and the remainder is attributed to primary demand effects (timing acceleration and quantity increases). The authors demonstrate that this result does not imply that if a brand gains 100 units in sales during a promotion, the other brands in the category lose 74 units. The authors offer a complementary decomposition measure based on unit sales. The measure shows the ratio of the current cross-brand unit sales loss to the current own-brand unit sales gain during promotion; the authors report empirical results for this measure. They also derive analytical expressions that transform the elasticity decomposition into a decomposition of unit sales effects. These expressions show the nature of the difference between the two decompositions. To gain insight into the magnitude of the difference, the authors apply these expressions to previously reported elasticity decomposition results and find that approximately 33% of the unit sales increase is attributable to losses incurred by other brands in the same category.

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