Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem.

Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach

List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

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Evolutionary algorithms for solving multi-objective problems

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.

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Evolving artificial neural networks

Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. In this paper, the basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective. The focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. Different techniques to implement these strongly related concepts will be discussed, and further important aspects such as constraint handling and preference articulation are treated as well. Finally, two applications will presented and some recent trends in the field will be outlined.

Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications

In the not so distant future, we expect analytic models to become a commodity. We envision having access to a large number of data-driven models, obtained by a combination of crowdsourcing, crowdservicing, cloud-based evolutionary algorithms, outsourcing, in-house development, and legacy models. In this new context, the critical question will be model ensemble selection and fusion, rather than model generation. We address this issue by proposing customized model ensembles on demand, inspired by Lazy Learning. In our approach, referred to as Lazy Meta-Learning, for a given query we find the most relevant models from a DB of models, using their meta-information. After retrieving the relevant models, we select a subset of models with highly uncorrelated errors. With these models we create an ensemble and use their meta-information for dynamic bias compensation and relevance weighting. The output is a weighted interpolation or extrapolation of the outputs of the models ensemble. Furthermore, the confidence interval around the output is reduced as we increase the number of uncorrelated models in the ensemble. We have successfully tested this approach in a power plant management application.

Advances in Computational Intelligence

Fitness Functions for Multiple Objective Optimization Problems: Combining Preferences with Pareto Rankings.

http://web.eng.fiu.edu/gaquan/Papers/quan2007ejor.pdf

Searching for multiobjective preventive maintenance schedules: Combining preferences with evolutionary algorithms

Introduction to Evolvable Hardware: A Practical Guide for Designing Self-Adaptive Systems (IEEE Press Series on Computational Intelligence)

PREFACE. ACKNOWLEDGMENTS. ACRONYMS. 1 INTRODUCTION. 1.1 Characteristics of Evolvable Circuits and Systems. 1.2 Why Evolvable Hardware Is Good (and Bad!). 1.3 Technology. 1.4 Evolvable Hardware vs. Evolved Hardware. 1.5 Intrinsic vs. Extrinsic Evolution. 1.6 Online vs. Offline Evolution. 1.7 Evolvable Hardware Applications. References. 2 FUNDAMENTALS OF EVOLUTIONARY COMPUTATION. 2.1 What Is an EA? 2.2 Components of an EA. 2.2.1 Representation. 2.2.2 Variation. 2.2.3 Evaluation. 2.2.4 Selection. 2.2.5 Population. 2.2.6 Termination Criteria. 2.3 Getting the EA to Work. 2.4 Which EA Is Best? References. 3 RECONFIGURABLE DIGITAL DEVICES. 3.1 Basic Architectures. 3.1.1 Programmable Logic Devices. 3.1.2 Field Programmable Gate Array. 3.2 Using Reconfigurable Hardware. 3.2.1 Design Phase. 3.2.2 Execution Phase. 3.3 Experimental Results. 3.4 Functional Overview of the POEtic Architecture. 3.4.1 Organic Subsystem. 3.4.2 Description of the Molecules. 3.4.3 Description of the Routing Layer. 3.4.4 Dynamic Routing. 3.5 Remarks. References. 4 RECONFIGURABLE ANALOG DEVICES. 4.1 Basic Architectures. 4.2 Transistor Arrays. 4.2.1 The NASA FTPA. 4.2.2 The Heidelberg FPTA. 4.3 Analog Arrays. 4.4 Remarks. References. 5 PUTTING EVOLVABLE HARDWARE TO USE. 5.1 Synthesis vs. Adaption. 5.2 Designing Self-Adaptive Systems. 5.2.1 Fault Tolerant Systems. 5.2.2 Real-Time Systems. 5.3 Creating Fault Tolerant Systems Using EHW. 5.4 Why Intrinsic Reconfiguration for Online Systems? 5.5 Quantifying Intrinsic Reconfiguration Time. 5.6 Putting Theory Into Practice. 5.6.1 Minimizing Risk With Anticipated Faults. 5.6.2 Minimizing Risk With Unanticipated Faults. 5.6.3 Suggested Practices. 5.7 Examples of EHW-Based Fault Recovery. 5.7.1 Population vs. Fitness-Based Designs. 5.7.2 EHW Compensators. 5.7.3 Robot Control. 5.7.4 The POEtic Project. 5.7.5 Embryo Development. 5.8 Remarks. References. 6 FUTURE WORK. 6.1 Circuit Synthesis Topics. 6.1.1 Digital Design. 6.1.2 Analog Design. 6.2 Circuit Adaption Topics. References. INDEX . ABOUT THE AUTHORS.

Garrison W. Greenwood

Papers

Advances in Computational Intelligence

Fitness Functions for Multiple Objective Optimization Problems: Combining Preferences with Pareto Rankings.

Searching for multiobjective preventive maintenance schedules: Combining preferences with evolutionary algorithms

Introduction to Evolvable Hardware: A Practical Guide for Designing Self-Adaptive Systems (IEEE Press Series on Computational Intelligence)

Introduction to Evolvable Hardware: A Practical Guide for Designing Self-Adaptive Systems