Future research in evolutionary computation

2004 ◽  
Author(s):  
Lawrence J Fogel
Keyword(s):  
Evolutionary Computation ◽  
Future Research

Genetic Improvement @ ICSE 2021

2021 ◽  
Vol 46 (4) ◽  
pp. 28-30
Author(s):  
Alexander E. I. Brownlee
Keyword(s):  
Software Engineering ◽  
Evolutionary Computation ◽  
Genetic Improvement ◽  
Future Research ◽  
Personal Perspective ◽  
International Conference ◽  
Keynote Presentation

Following Dr. Stephanie Forrest of Arizona State University's keynote presentation there was a wide ranging discussion at the tenth international Genetic Improvement workshop, GI-2021 @ ICSE (held as part of the International Conference on Software Engineering on Sunday 30th May 2021). Topics included a growing range of target systems and appli- cations, algorithmic improvements, wide-ranging questions about how other elds (especially evolutionary computation) can inform advances in GI, and about how GI is 'branded' to other disciplines. We give a personal perspective on the workshop's proceedings, the discussions that took place, and resulting prospective directions for future research.

scholarly journals A Survey on Evolutionary Computation Approaches to Feature Selection

2021 ◽  
Author(s):  
Bing Xue ◽  
Mengjie Zhang ◽  
William Browne ◽  
X Yao
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Evolutionary Computation ◽  
State Of The Art ◽  
Search Space ◽  
Mining Machine ◽  
Future Research ◽  
Selection Problems ◽  
Personal Use ◽  
Index Terms

Feature selection is an important task in data miningand machine learning to reduce the dimensionality of the dataand increase the performance of an algorithm, such as a clas-sification algorithm. However, feature selection is a challengingtask due mainly to the large search space. A variety of methodshave been applied to solve feature selection problems, whereevolutionary computation techniques have recently gained muchattention and shown some success. However, there are no compre-hensive guidelines on the strengths and weaknesses of alternativeapproaches. This leads to a disjointed and fragmented fieldwith ultimately lost opportunities for improving performanceand successful applications. This paper presents a comprehensivesurvey of the state-of-the-art work on evolutionary computationfor feature selection, which identifies the contributions of thesedifferent algorithms. In addition, current issues and challengesare also discussed to identify promising areas for future research. Index Terms—Evolutionary computation, feature selection,classification, data mining, machine learning. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals A Survey on Evolutionary Computation Approaches to Feature Selection

2021 ◽  
Author(s):  
Bing Xue ◽  
Mengjie Zhang ◽  
William Browne ◽  
X Yao
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Evolutionary Computation ◽  
State Of The Art ◽  
Search Space ◽  
Mining Machine ◽  
Future Research ◽  
Selection Problems ◽  
Personal Use ◽  
Index Terms

Feature selection is an important task in data miningand machine learning to reduce the dimensionality of the dataand increase the performance of an algorithm, such as a clas-sification algorithm. However, feature selection is a challengingtask due mainly to the large search space. A variety of methodshave been applied to solve feature selection problems, whereevolutionary computation techniques have recently gained muchattention and shown some success. However, there are no compre-hensive guidelines on the strengths and weaknesses of alternativeapproaches. This leads to a disjointed and fragmented fieldwith ultimately lost opportunities for improving performanceand successful applications. This paper presents a comprehensivesurvey of the state-of-the-art work on evolutionary computationfor feature selection, which identifies the contributions of thesedifferent algorithms. In addition, current issues and challengesare also discussed to identify promising areas for future research. Index Terms—Evolutionary computation, feature selection,classification, data mining, machine learning. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals A survey on evolutionary computation for complex continuous optimization

2021 ◽  
Author(s):  
Zhi-Hui Zhan ◽  
Lin Shi ◽  
Kay Chen Tan ◽  
Jun Zhang
Keyword(s):  
Evolutionary Computation ◽  
Large Scale ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Future Research ◽  
Practical Applications ◽  
Fast Development ◽  
Comprehensive Survey ◽  
Continuous Optimization Problems ◽  
Expensive Optimization

AbstractComplex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

User Fatigue in Interactive Evolutionary Computation

2011 ◽  
Vol 48-49 ◽  
pp. 1333-1336 ◽  
Author(s):  
Jun Rong Yan ◽  
Yong Min
Keyword(s):  
Evolutionary Computation ◽  
Future Research ◽  
Interactive Evolutionary Computation ◽  
User Fatigue ◽  
The Impact

User fatigue is an important issue in interactive evolutionary computation (IEC). A user’s evaluation will guide the evolution in IEC and a tired user probably misleads the algorithm. Firstly, the impact of the user fatigue is given. Secondly, the cause of the user fatigue is discussed: he/she will have to keep rational, which means that the user will have to keep the consistency between his/her evaluation and preference. And the necessity for the user to keep rational is also analyzed, which will ensure IEC to converge to the user-most-satisfactory individuals. The study of user fatigue established necessary foundation for future research.

Applications of Evolutionary Neural Networks for Sales Forecasting of Fashionable Products

2010 ◽  
pp. 387-403 ◽  
Author(s):  
Yong Yu ◽  
Tsan-Ming Choi ◽  
Kin-Fan Au ◽  
Zhan-Li Sun
Keyword(s):  
Neural Network ◽  
Evolutionary Computation ◽  
Topology Design ◽  
Future Research ◽  
Binary Representation ◽  
Evolutionary Neural Network ◽  
Network Connection ◽  
Forecasting System ◽  
Future Research Directions ◽  
Artificial Neural Network Ann

The evolutionary neural network (ENN), which is the hybrid combination of evolutionary computation and neural network, is a suitable candidate for topology design, and is widely adopted. An ENN approach with a direct binary representation to every single neural network connection is proposed in this chapter for sales forecasting of fashionable products. In this chapter, the authors will first explore the details on how an evolutionary computation approach can be applied in searching for a desirable network structure for establishing the appropriate sales forecasting system. The optimized ENN structure for sales forecasting is then developed. With the use of real sales data, the authors compare the performances of the proposed ENN forecasting scheme with several traditional methods which include artificial neural network (ANN) and SARIMA. The authors obtain the conditions in which their proposed ENN outperforms other methods. Insights regarding the applications of ENN for forecasting sales of fashionable products are generated. Finally, future research directions are outlined.

scholarly journals A Review of Gait Optimization Based on Evolutionary Computation

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Daoxiong Gong ◽  
Jie Yan ◽  
Guoyu Zuo
Keyword(s):  
Evolutionary Computation ◽  
Optimization Problem ◽  
Legged Robots ◽  
Future Research ◽  
Genetic Operators ◽  
Fitness Functions ◽  
And Control ◽  
Gait Optimization ◽  
Selection Of

Gait generation is very important as it directly affects the quality of locomotion of legged robots. As this is an optimization problem with constraints, it readily lends itself to Evolutionary Computation methods and solutions. This paper reviews the techniques used in evolution-based gait optimization, including why Evolutionary Computation techniques should be used, how fitness functions should be composed, and the selection of genetic operators and control parameters. This paper also addresses further possible improvements in the efficiency and quality of evolutionary gait optimization, some problems that have not yet been resolved and the perspectives for related future research.

Parasite defense mechanisms in bees: behavior, immunity, antimicrobials, and symbionts

2019 ◽  
Vol 4 (1) ◽  
pp. 59-76 ◽  
Author(s):  
Alison E. Fowler ◽  
Rebecca E. Irwin ◽  
Lynn S. Adler
Keyword(s):  
Defense Mechanisms ◽  
Poor Quality ◽  
Future Research ◽  
Immune Priming ◽  
Social Bees ◽  
Bee Health ◽  
Landscape Scales ◽  
And Function ◽  
Solitary Species

Parasites are linked to the decline of some bee populations; thus, understanding defense mechanisms has important implications for bee health. Recent advances have improved our understanding of factors mediating bee health ranging from molecular to landscape scales, but often as disparate literatures. Here, we bring together these fields and summarize our current understanding of bee defense mechanisms including immunity, immunization, and transgenerational immune priming in social and solitary species. Additionally, the characterization of microbial diversity and function in some bee taxa has shed light on the importance of microbes for bee health, but we lack information that links microbial communities to parasite infection in most bee species. Studies are beginning to identify how bee defense mechanisms are affected by stressors such as poor-quality diets and pesticides, but further research on this topic is needed. We discuss how integrating research on host traits, microbial partners, and nutrition, as well as improving our knowledge base on wild and semi-social bees, will help inform future research, conservation efforts, and management.

Dicarbonyl derived post-translational modifications: chemistry bridging biology and aging-related disease

2020 ◽  
Vol 64 (1) ◽  
pp. 97-110
Author(s):  
Christian Sibbersen ◽  
Mogens Johannsen
Keyword(s):  
Mass Spectrometry ◽  
Future Research ◽  
Amino Acid Residues ◽  
Post Translational Modification ◽  
Dicarbonyl Compounds ◽  
Protein Targets ◽  
Post Translational Modifications ◽  
Reactive Protein ◽  
Glycation End Products ◽  
Direct Mass Spectrometry

Abstract In living systems, nucleophilic amino acid residues are prone to non-enzymatic post-translational modification by electrophiles. α-Dicarbonyl compounds are a special type of electrophiles that can react irreversibly with lysine, arginine, and cysteine residues via complex mechanisms to form post-translational modifications known as advanced glycation end-products (AGEs). Glyoxal, methylglyoxal, and 3-deoxyglucosone are the major endogenous dicarbonyls, with methylglyoxal being the most well-studied. There are several routes that lead to the formation of dicarbonyl compounds, most originating from glucose and glucose metabolism, such as the non-enzymatic decomposition of glycolytic intermediates and fructosyl amines. Although dicarbonyls are removed continuously mainly via the glyoxalase system, several conditions lead to an increase in dicarbonyl concentration and thereby AGE formation. AGEs have been implicated in diabetes and aging-related diseases, and for this reason the elucidation of their structure as well as protein targets is of great interest. Though the dicarbonyls and reactive protein side chains are of relatively simple nature, the structures of the adducts as well as their mechanism of formation are not that trivial. Furthermore, detection of sites of modification can be demanding and current best practices rely on either direct mass spectrometry or various methods of enrichment based on antibodies or click chemistry followed by mass spectrometry. Future research into the structure of these adducts and protein targets of dicarbonyl compounds may improve the understanding of how the mechanisms of diabetes and aging-related physiological damage occur.

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