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A Personal Perspective on evolutionary Computation: A 35-Year Journey

evolutionary COMPUTATION 2023年第2期31卷 123-155页

作者： Michalewicz, Zbigniew Complexica Level 29 Charles St West Lakes SA 5021 Australia

This paper presents a personal account of the author's 35 years "adventure" with evolutionary Computation-from the first encounter in 1988 and many years of academic research through to working full-time in business-successfully implementing evolutionary algorithms for some of the world's largest corporations. The paper concludes with some observations and insights.

关键词： evolutionary algorithms

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Automatic design of evolutionary algorithms for multi-objective combinatorial optimization

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8672卷 508-517页

作者： Bezerra, Leonardo C. T. López-Ibáñez, Manuel Stützle, Thomas Brussels Belgium

Multi-objective evolutionary algorithms (MOEAs) have been the subject of a large research effort over the past two decades. Traditionally, theseMOEAs have been seen as monolithic units, and their study was focused on comparing them as blackboxes. More recently, a componentwise view of MOEAs has emerged, with flexible frameworks combining algorithmic components from different MOEAs. The number of available algorithmic components is large, though, and an algorithm designer working on a specific application cannot analyze all possible combinations. In this paper, we investigate the automatic design ofMOEAs, extending previous work on other multi-objective metaheuristics. We conduct our tests on four variants of the permutation flowshop problem that differ on the number and nature of the objectives they ***, given the different characteristics of the variants, we also investigate the performance of an automatic MOEA designed for the multi-objective PFSP in general. Our results show that the automatically designed MOEAs are able to outperform six traditional MOEAs, confirming the importance and efficiency of this design methodology. © Springer International Publishing Switzerland 2014.

关键词： evolutionary algorithms

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evolutionary multi-objective algorithms for feed resource allocation in dairy systems

Evolutionary multi-objective algorithms for feed resource al...

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IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Notte, Gaston Chilibroste, Pablo Pedemonte, Martin Cancela, Hector UdelaR CENUR Litoral Norte Dept Ingn Biol Paysandu Uruguay UdelaR Fac Agron Montevideo Uruguay UdelaR Fac Ingn Montevideo Uruguay

ISBN: (纸本)9781728188645

In grassland-based dairy systems, determining how to rotate the cows among fields for grazing, how much concentrate to supply and the correct stocking rate to be used are important decisions that impact on the efficiency of the system. Considering the presence of conflictive objectives, a multi-objective approach is therefore the natural way of facing the problem. Due to the computational difficulty of finding the full solution set (the Pareto front) of multi-objective models, it is usually necessary to employ algorithms giving a good approximation of this set. In particular, a number of multi-objective evolutionary algorithms with different characteristics have been proposed in the general optimization literature;but there is no current study of which is the most appropriate one for feed resource allocation in dairy systems. In this work, we present the performance evaluation of four multi-objective evolutionary algorithms to generate an approximation of the Pareto front of the feed resource allocation problem in dairy systems. Two classical genetic algorithms (NSGA-II and SPEA-2) and two differential evolution (DE) algorithms (GDE-3 and a Pareto-based DE) were used. To evaluate the algorithms, two experiments based on scenarios constructed from real data were performed. The comparison took into account running times, objective function values attained, Pareto front comparisons, and approximation quality measures based on four different metrics. From the results we conclude that the SPEA-2 is the algorithm that obtains the best quality performance for the problem under study, but also the slowest one, opening a future work opportunity of improving its computational performance.

关键词： Resource Allocation Dairy Production evolutionary algorithms Multi-Objective Pareto Front Quality Metrics

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Subpopulation diversity based setting success rate of migration for distributed evolutionary algorithms

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8862卷 1-12页

作者： Li, Chengjun Chen, Zhe Gu, Shuhua Hu, Guangdao Shan, Hongyuan China University of Geosciences School of Computer Science Wuhan430074 China China University of Geosciences Wuhan430074 China

In most of distributed evolutionary algorithms (DEAs), migration interval is used to decide the frequent of migration. Nevertheless, a predetermined interval cannot match the dynamic situation of evolution. Consequently, migration may happen at a wrong moment and just exert a negative influence to evolution. In this paper, a scheme of setting the success rate of migration based on subpopulation diversity is proposed. In the scheme, migration still happens at intervals, but the probability of immigrants entering the target subpopulation will be decided by the diversity of this subpopulation. An analysis shows that the extra time consumption for our scheme in a DEA is acceptable. In our experiments, outcomes of the DEA based on the proposed scheme are compared with those of a traditional DEA on six benchmark instances of the Traveling Salesman Problem. The results show that the former performs better than its peer. Moreover, the DEA based on our scheme shows an advantage in stability. © Springer International Publishing Switzerland 2014.

关键词： evolutionary algorithms

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Neuro-augmented vision for evolutionary robotics

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MACHINE VISION AND APPLICATIONS 2023年第6期34卷 1-23页

作者： Watt, Nathan du Plessis, Mathys C. Nelson Mandela Univ Dept Comp Sci Gqeberha South Africa

This paper presents neuro-augmented vision for evolutionary robotics (NAVER), which aims to address the two biggest challenges in camera-equipped robot evolutionary controllers. The first challenge is that camera images typically require many inputs from the controller, which greatly increases the complexity of optimising the search space. The second challenge is that evolutionary controllers often cannot bridge the reality gap between simulation and the real world. This method utilises a variational autoencoder to compress the camera image into smaller input vectors that are easier to manage, while still retaining the relevant information of the original image. Automatic encoders are also used to remove unnecessary details from real-world images, in order to better align with images generated by simple visual simulators. NAVER is used to evolve the controller of a robot, which only uses camera inputs to navigate the maze based on visual cues and avoid collisions. The experimental results indicate that the controller evolved in simulation and transferred to the physical robot, where it successfully performed the same navigation task. The controller can navigate the maze using only visual information. The controller responds to visual cues and changes its behaviour accordingly. NAVER has shown great potential as it has successfully completed (so far) the most complex vision-based task controller in evolutionary robotics literature.

关键词： Robotics evolutionary algorithms Robot vision Deep neural networks

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Classification model-based assisted preselection and environment selection approach for evolutionary expensive bilevel optimization

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APPLIED INTELLIGENCE 2023年第23期53卷 28377-28400页

作者： Lin, Libin Liu, Ting Leng, Jiewu Yao, Shaowen Zhang, Hao Wei, Lijun Liu, Qiang Guangdong Univ Technol Guangdong Prov Key Lab Comp Integrated Mfg Syst State Key Lab Precis Elect Mfg Technol & Equipment Guangzhou 510006 Peoples R China

Bilevel evolutionary algorithms (BLEAs) are a plausible approach for bilevel optimization. However, these algorithms require many fitness evaluations (FEs) and might become unusable if the fitness evaluations are computationally expensive. Therefore, reducing the number of FEs is crucial for designing a BLEA for expensive bilevel optimization (EBLOP). The surrogate-assisted optimization and knowledge transfer mechanisms used in BLEAs have been proven to reduce the number of FEs. This paper proposes a surrogate-assisted bilevel improved multioperator differential evolution algorithm (SA-BL-IMODE), which integrates a classification model-based assisted preselection and environment selection strategy (CPES) for EBLOP. In CPES, promising candidate solutions are prescreened by a classification model, filtering out some unpromising candidate solutions without performing FEs, thus improving the algorithm's performance. Moreover, the classification model also assists environment selection by directly discarding the unpromising offspring solutions before FEs are performed, thus reducing the number of FEs in each iteration. Additionally, an enhanced direct neighbor solution transfer (EDST) mechanism is proposed to identify and utilize the interactions between upper-level and lower-level variables for acquiring knowledge, improving knowledge quality, and reducing the number of FEs further. Experimental studies on two test suite benchmark problems are conducted, and the proposed method is compared with nine state-of-the-art algorithms. The experimental results demonstrate the effectiveness of the proposed mechanisms and show that SA-BL-IMODE has a significant advantage over existing algorithms for expensive bilevel optimization.

关键词： Expensive bilevel optimization Surrogate-assisted optimization evolutionary algorithms Classification Knowledge transfer

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Solution of constrained mixed-integer multi-objective optimal power flow problem considering the hybrid multi-objective evolutionary algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2023年第1期17卷 66-90页

作者： Ali, Aamir Abbas, Ghulam Keerio, Muhammad Usman Koondhar, Mohsin Ali Chandni, Kiran Mirsaeidi, Sohrab Quaid E Awam Univ Engn Sci & Technol Dept Elect Engn Nawabshah Pakistan Southeast Univ Sch Elect Engn Nanjing Peoples R China Beijing Jiaotong Univ Sch Elect Engn Beijing Peoples R China

An Optimal power flow (OPF) is non-linear and constrained multi-objective problem. OPF problems are expensive and evolutionary algorithms (EAs) are computationally complex to obtain uniformly distributed and global Pareto front (PF). Therefore, here, hybrid two-phase algorithm integrated with parameter less constraint technique is applied to solve OPF problem. Proposed technique combines single and multi-objective EAs to find better convergence and evenly distributed PF. For the validation and effectiveness of proposed algorithm, various conflicting objective functions are formulated and implemented on IEEE 30 and 300-bus network. Each case is independently run twenty times. Hyper volume indicator technique is employed to find the best PF, and the best-compromised solution is obtained by using fuzzy decision-making technique. Recently, maximum integration of wind and solar power is highly encouraged. Complexity of OPF is increased with the integration of uncertain renewable energy resources. Hence, 30-bus test system is modified by replacing some conventional generators with the wind and solar generation. Uncertainties in wind, solar and load demand are modelled by appropriate probability distribution functions. Simulation results show that the proposed method can find the near global PF of highly complex problems subject to satisfying all the operational constraints.

关键词： OPF problem 30-bus test system solar power generation Optimisation techniques Wind power plants uniformly distributed Pareto front Solar power stations and photovoltaic power systems Pareto optimisation solar power global Pareto front wind power generation evolutionary computation conventional generators constrained mixed-integer multiobjective optimal power flow problem fuzzy decision-making technique Nonlinear and functional equations (numerical analysis) IEEE 30 bus network evolutionary algorithms probability distribution functions hybrid two-phase algorithm IEEE 300-bus network wind power plants uncertain renewable energy resources hybrid multiobjective evolutionary problem integer programming probability parameter less constraint technique load flow fuzzy set theory nonlinear equations wind power nonlinear model global PF decision making hyper volume indicator technique IEEE standards multiobjective EAs load demand highly complex problems subject computational complexity Combinatorial mathematics hybrid power systems

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A pairwise comparison based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization

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SWARM AND evolutionary COMPUTATION 2023年 80卷

作者： Tian, Ye Hu, Jiaxing He, Cheng Ma, Haiping Zhang, Limiao Zhang, Xingyi Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China Anhui Univ Inst Phys Sci Hefei Peoples R China Anhui Univ Inst Informat Technol Hefei Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan Peoples R China Anhui Univ Sch Artificial Intelligence Hefei Peoples R China

Multi-objective optimization problems in many real-world applications are characterized by computationally or economically expensive objectives, which cannot provide sufficient function evaluations for evolutionary algorithms to converge. Thus, a variety of surrogate models have been employed to provide much more virtual evaluations. Most existing surrogate models are essentially regressors or classifiers, which may suffer from low reliability in the approximation of complex objectives. In this paper, we propose a novel surrogate-assisted evolutionary algorithm, which employs a surrogate model to conduct pairwise comparisons between candidate solutions, rather than directly predicting solutions' fitness values. In comparison to regression and classification models, the proposed pairwise comparison based model can better balance between positive and negative samples, and may be directly used, reversely used, or ignored according to its reliability in model management. As demonstrated by the experimental results on abundant benchmark and real-world problems, the proposed surrogate model is more accurate than popular surrogate models, leading to performance superiority over state-of-the-art surrogate models.

关键词： evolutionary algorithms Expensive multi-objective optimization Surrogate-assisted optimization Pairwise comparison

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Intelligent mutation based evolutionary optimization algorithm for genomics and precision medicine

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FUNCTIONAL & INTEGRATIVE GENOMICS 2024年第4期24卷 1-15页

作者： Singh, Shailendra Pratap Yadav, Dileep Kumar Chamran, Mohammad Kazem Perera, Darshika G. Bennett Univ SCSET Greater Noida Uttar Pradesh India City Univ FIT Petaling Jaya Malaysia Univ Colorado Colorado Springs Dept Elect & Comp Engn Colorado Springs CO 80918 USA

In this paper, genomics and precision medicine have witnessed remarkable progress with the advent of high-throughput sequencing technologies and advances in data analytics. However, because of the data's great dimensionality and complexity, the processing and interpretation of large-scale genomic data present major challenges. In order to overcome these difficulties, this research suggests a novel Intelligent Mutation-Based evolutionary Optimization Algorithm (IMBOA) created particularly for applications in genomics and precision medicine. In the proposed IMBOA, the mutation operator is guided by genome-based information, allowing for the introduction of variants in candidate solutions that are consistent with known biological processes. The algorithm's combination of Differential Evolution with this intelligent mutation mechanism enables effective exploration and exploitation of the solution space. Applying a domain-specific fitness function, the system evaluates potential solutions for each generation based on genomic correctness and fitness. The fitness function directs the search toward ideal solutions that achieve the problem's objectives, while the genome accuracy measure assures that the solutions have physiologically relevant genomic properties. This work demonstrates extensive tests on diverse genomics datasets, including genotype-phenotype association studies and predictive modeling tasks in precision medicine, to verify the accuracy of the proposed approach. The results demonstrate that, in terms of precision, convergence rate, mean error, standard deviation, prediction, and fitness cost of physiologically important genomic biomarkers, the IMBOA consistently outperforms other cutting-edge optimization methods.

关键词： Intelligent mutation evolutionary algorithms Genome data sets Cancer precision

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Advanced design optimisation by means of multiobjective evolutionary algorithms: The case of two real world applications

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Key Engineering Materials 2014年第1期572卷 589-592页

作者： Benkhelifa, Elhadj Tiwari, Ashutosh AbdeI-Maguid, Mohamed University Campus Suffolk 1P4 1QJ United Kingdom Cranfield University MK43 OAL United Kingdom Staffordshire University 5T18 OAD United Kingdom

ISBN: (纸本)9783037858196

The Design Optimisation (DO) of Complex Systems is often a multidisciplinary task and involves multiple conflicting objectives and design constraints, where conventional methods cannot solve efficiently. This paper presents Advanced DO by Means of Evolutional algorithms in two Real World Applications Electronics and Micro-Electro-Mechanical-Systems (MEMS). The former is presented in the context of multi-objective evolutionary synthesis and optimisation of analogue systems. As for the latter, DO of MEMS bio-mimetically is a very novel area of research, Which addresses the compelling change in the traditional landscape of the associated research disciplines by seeking to provide a novel biologically inspired computational platform for DO of micro-scale designs. This paper presents the latest advancements in the application of EAs in the DO of MEMS and analogue electronic systems and the emergence of the new area of Multidisciplinary Optimisation'. © 2014 Trans Tech Publications, Switzerland.

关键词： evolutionary algorithms

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