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  1. Evolutionary optimization : the [mu]GP toolkit

    Sanchez, Ernesto
    New York : Springer, ©2011.

    "Evolutionary Optimization: The A GP Toolkit" offers a cohesive, thorough exploration of the scattered material on A GP features that has been presented to and validated by the scientific community, giving a fresh perspective on the subject. The book begins with an overview of the most popular techniques of evolutionary computation, followed by a discussion of the theoretical and practical aspects of A GP. The design and implementation of the tool are described, providing the reader with a conceptual model of the approach and a reference for operation. Several examples are detailed, starting from the informal description of the target problem, proceeding to the writing of syntactic constraints and configuration and to fitness evaluation, ending with actual results. Practitioners, researchers and students will find this volume a valuable reference in working with A GP, whether for the resolution of real-world problems, or for testing theoretical ideas.

    Online SpringerLink

  2. Applications of evolutionary computation : 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30-April 1, 2016, Proceedings, Part II

    Cham : Springer, 2016.

    The two volumes LNCS 9597 and 9598 constitute the refereed conference proceedings of the 19th European Conference on the Applications of Evolutionary Computation, EvoApplications 2016, held in Porto, Portugal, in March/April 2016, co-located with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 57 revised full papers presented together with 17 poster papers were carefully reviewed and selected from 115 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for risk management, security and defence applications), EvoROBOT (evolutionary robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).

    Online SpringerLink

  3. Industrial applications of evolutionary algorithms

    Sanchez, Ernesto, 1974-
    Heidelberg ; New York : Springer, ©2012.

    Annotation "Industrial applications of evolutionary algorithms" is intended as a resource for both experienced users of evolutionary algorithms and researchers that are beginning to approach these fascinating optimization techniques. Experienced users will find interesting details of real-world problems, advice on solving issues related to fitness computation or modeling, and suggestions on how to set the appropriate parameters to reach optimal solutions. Beginners will find a thorough introduction to evolutionary computation, and a complete presentation of several classes of evolutionary algorithms exploited to solve different problems. Inside, scholars will find useful examples on how to fill the gap between purely theoretical examples and industrial problems. The collection of case studies presented is also extremely appealing for anyone interested in Evolutionary Computation, but without direct access to extensive technical literature on the subject. After the introduction, each chapter in the book presents a test case, and is organized so that it can be read independently from the rest: all the information needed to understand the problem and the approach is reported in each part. Chapters are grouped by three themes of particular interest for real-world applications, namely prototype-based validation, reliability and test generation. The authors hope that this volume will help to expose the flexibility and efficiency of evolutionary techniques, encouraging more companies to adopt them; and that, most of all, you will enjoy your reading"Industrial applications of evolutionary algorithms" is intended as a resource for both experienced users of evolutionary algorithms and researchers that are beginning to approach these fascinating optimization techniques. Experienced users will find interesting details of real-world problems, advice on solving issues related to fitness computation or modeling, and suggestions on how to set the appropriate parameters to reach optimal solutions. Beginners will find a thorough introduction to evolutionary computation, and a complete presentation of several classes of evolutionary algorithms exploited to solve different problems. Inside, scholars will find useful examples on how to fill the gap between purely theoretical examples and industrial problems. The collection of case studies presented is also extremely appealing for anyone interested in Evolutionary Computation, but without direct access to extensive technical literature on the subject. After the introduction, each chapter in the book presents a test case, and is organized so that it can be read independently from the rest: all the information needed to understand the problem and the approach is reported in each part. Chapters are grouped by three themes of particular interest for real-world applications, namely prototype-based validation, reliability and test generation. The authors hope that this volume will help to expose the flexibility and efficiency of evolutionary techniques, encouraging more companies to adopt them; and that, most of all, you will enjoy your reading.

    Online SpringerLink

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