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RE: [GP] New Book: Scalable Optimization via Probabilistic Modeling

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  • John Koza
    Congratulations. Dr. John R. Koza PO Box K Los Altos, CA 94023 USA Phone: 650-941-0336 Fax: 650-941-9430 E-Mail: john@johnkoza.com URL: www.johnkoza.com URL:
    Message 1 of 2 , Oct 31, 2006
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      Congratulations.

      Dr. John R. Koza
      PO Box K
      Los Altos, CA 94023 USA
      Phone: 650-941-0336
      Fax: 650-941-9430
      E-Mail: john@...
      URL: www.johnkoza.com
      URL: www.genetic-programming.com
      URL: www.NationalPopularVote.com

      -----Original Message-----
      From: Martin Pelikan [mailto:pelikan@...]
      Sent: Monday, October 30, 2006 2:37 PM
      To: genetic_programming@yahoogroups.com
      Cc: pelikan@...
      Subject: [GP] New Book: Scalable Optimization via Probabilistic Modeling

      The book "Scalable Optimization via Probabilistic Modeling: From
      Algorithms to Applications" edited by M. Pelikan, K. Sastry, and E.
      Cantu-Paz has just been published by Springer.

      Short Description:
      Estimation of distribution algorithms combine evolutionary computation and
      machine learning to provide a class of robust and scalable optimization
      techniques applicable to broad classes of difficult problems. Scalable
      optimization via Probabilistic Modeling compiles articles by some of the
      leading experts in academia and industry that range from design and
      analysis to efficiency enhancement and real-world applications of
      estimation of distribution algorithms. The book is written for the general
      audience and should be of interest for optimization researchers and
      practitioners alike.

      Table of Contents:
      * Introduction
      Martin Pelikan, Kumara Sastry, Erick Cantu-Paz

      * The Factorized Distribution Algorithm and the Minimum Relative
      Entropy Principle
      Heinz Muehlenbein, Robin Hoens

      * Linkage Learning via Probabilistic Modeling in the ECGA
      Georges R. Harik, Fernando G. Lobo, Kumara Sastry

      * Hierarchical Bayesian Optimization Algorithm
      Martin Pelikan, David E. Goldberg

      * Numerical Optimization with Real-Valued Estimation-of-Distribution
      Algorithms
      Peter A.N. Bosman, Dirk Thierens

      * A Survey of Probabilistic Model Building Genetic Programming
      Yin Shan, Robert I. McKay, Daryl Essam, Hussein A. Abbass

      * Efficiency Enhancement of Estimation of Distribution Algorithms
      Kumara Sastry, Martin Pelikan, David E. Goldberg

      * Design of Parallel Estimation of Distribution Algorithms
      Jiri Ocenasek, Erick Cantu-Paz, Martin Pelikan, Josef Schwarz

      * Incorporating a priori Knowledge in Probabilistic-Model Based
      Optimization
      Shumeet Baluja

      * Multiobjective Estimation of Distribution Algorithms
      Martin Pelikan, Kumara Sastry, David E. Goldberg

      * Effective and Reliable Online Classification Combining XCS with
      EDA Mechanisms
      Martin Butz, Martin Pelikan, Xavier Llora, David E. Goldberg

      * Military Antenna Design Using a Simple Genetic Algorithm and hBOA
      Tian-Li Yu, Scott Santarelli, David E. Goldberg

      * Feature Subset Selection with Hybrids of Filters and Evolutionary
      Algorithms
      Erick Cantu-Paz

      * BOA for Nurse Scheduling
      Jingpeng Li, Uwe Aickelin

      * Searching for Ground States of Ising Spin Glasses with Hierarchical
      BOA and Cluster Exact Approximation
      Martin Pelikan, Alexander K. Hartmann

      More Information:
      http://medal.cs.umsl.edu/scalable-optimization-book/

      ----------------------------------------------------------
      Martin Pelikan
      Missouri Estimation of Distribution Algorithms Lab (MEDAL)
      Dept. of Mathematics and Computer Science
      University of Missouri at St. Louis, CCB 320
      One University Blvd.
      St. Louis, MO 63121

      Phone/Fax: (314) 516 6348 / (314) 516-5400

      E-mail: pelikan@...
      WWW: http://www.cs.umsl.edu/~pelikan/
      http://medal.cs.umsl.edu/
      ----------------------------------------------------------




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