New IlliGAL Books

[Martin Pelikan <pelikan@illigal.ge.uiuc.edu>]

The Illinois Genetic Algorithms Laboratory (IlliGAL) is pleased to
announce the publication of the following new books authored by
IlliGAL members and alumni. These and other IlliGAL books can be 
ordered through IlliGAL web site at
http://www-illigal.ge.uiuc.edu/books.html

Omega : A Competent Genetic Algorithm for Solving Permutation and 
Scheduling Problems
Knjazew, Dimitri

Genetic Algorithms and Evolutionary Computation, Volume 6. 2001.
Kluwer Academic Pub; ISBN:0792374606

Book description:
OmeGA: A Competent Genetic Algorithm for Solving Permutation and 
Scheduling Problems addresses two increasingly important areas in 
GA implementation and practice. OmeGA, or the ordering messy genetic 
algorithm, combines some of the latest in competent GA technology 
to solve scheduling and other permutation problems. Competent GAs 
are those designed for principled solutions of hard problems, quickly, 
reliably, and accurately. Permutation and scheduling problems are 
difficult combinatorial optimization problems with commercial import 
across a variety of industries. This book approaches both subjects 
systematically and clearly. The first part of the book presents the 
clearest description of messy GAs written to date along with an 
innovative adaptation of the method to ordering problems. The second 
part of the book investigates the algorithm on boundedly difficult 
test functions, showing principled scale up as problems become harder 
and longer. Finally, the book applies the algorithm to a test function 
drawn from the literature of scheduling. 


Anticipatory Learning Classifier Systems
Butz, Martin V.

Genetic Algorithms and Evolutionary Computation, Volume 4. 2001.
Kluwer Academic Pub; ISBN:0792376307

Book description:
Anticipatory Learning Classifier Systems describes the state of the
art of anticipatory learning classifier systems-adaptive rule learning
systems that autonomously build anticipatory environmental models. An
anticipatory model specifies all possible action-effects in an
environment with respect to given situations. It can be used to
simulate anticipatory adaptive behavior. Anticipatory Learning
Classifier Systems highlights how anticipations influence cognitive
systems and illustrates the use of anticipations for (1) faster
reactivity, (2) adaptive behavior beyond reinforcement learning, (3)
attentional mechanisms, (4) simulation of other agents and (5) the
implementation of a motivational module. The book focuses on a
particular evolutionary model learning mechanism, a combination of a
directed specializing mechanism and a genetic generalizing
mechanism. Experiments show that anticipatory adaptive behavior can be
simulated by exploiting the evolving anticipatory model for even
faster model learning, planning applications, and adaptive behavior
beyond reinforcement learning. Anticipatory Learning Classifier
Systems gives a detailed algorithmic description as well as a program
documentation of a C++ implementation of the system. It is an
excellent reference for researchers interested in adaptive behavior
and machine learning from a cognitive science perspective as well as
those who are interested in combining evolutionary learning mechanisms
for learning and optimization tasks. 

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 Martin Pelikan
 Illinois Genetic Algorithms Laboratory
 University of Illinois at Urbana Champaign
 117 Transportation Building 
 104 S. Mathews Avenue, Urbana, IL 61801
 Tel: (217) 333-2346, FAX: (217) 244-5705
 E-mail: pelikan@illigal.ge.uiuc.edu
 WWW: http://www-illigal.ge.uiuc.edu/~pelikan/
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