Category: Genetic algorithms

The book offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system – the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland’s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.

Book Description Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The primary focus of the book is on two algorithms that replace traditional variation operators of evolutionary algorithms by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA) . They provide a scalable solution to a broad class of problems. The book provides an overview of evolutionary algorithms that use probabilistic models to guide their search, motivates and describes BOA and hBOA in a way accessible to a wide audience and presents numerous results confirming that they are revolutionary approaches to black-box optimization.

Pelikan, Martin

From the author:
Presents a number of traditional and nontraditional (genetic algorithms and simulated annealing) optimization techniques in an easy-to-understand step-by-step format. Algorithms are supported with numerical examples and computer codes. Note: This book is not available at Amazon.com.

Book Description
In the field of genetic and evolutionary algorithms (GEAs), much theory and empirical study has been heaped upon operators and test problems, but problem representation has often been taken as given. This monograph breaks with this tradition and studies a number of critical elements of a theory of representations for GEAs and applies them to the empirical study of various important idealized test functions and problems of commercial import. The book considers basic concepts of representations, such as redundancy, scaling and locality and describes how GEAs’ performance is influenced. Using the developed theory representations can be analyzed and designed in a theory-guided manner. The theoretical concepts are used as examples for efficiently solving integer optimization problems and network design problems. The results show that proper representations are crucial for GEAs’ success.

Rothlauf, Franz.

Book Summary THE DESIGN OF INNOVATION shows how to design and implement competent genetic algorithms—genetic algorithms that solve hard problems quickly, reliably, and accurately—and how the invention of competent genetic algorithms amounts to the creation of an effective computational theory of human innovation. For the specialist in genetic algorithms and evolutionary computation, this book combines over two decades of hard-won research results in a single volume to provide a comprehensive step-by-step guide to designing genetic algorithms that scale well with problem size and difficulty. For the innovation researcher—whether from the social and behavioral sciences, the natural sciences, the humanities, or the arts—this unique book gives a consistent and valuable mathematical and computational viewpoint for understanding certain aspects of human innovation. For all readers, THE DESIGN OF INNOVATION provides an entrée into the world of competent genetic algorithms and innovation through a methodology of invention borrowed from the Wright brothers. Combining careful decomposition, cost-effective, little analytical models, and careful design, the road to competence is paved with easily understood examples, simulations, and results from the literature.

Genetic Algorithms and Evolutionary Computation, Volume 6.

Book Description
OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problemsaddresses 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.

Knjazew, Dimitri.

Genetic Algorithms and Evolutionary Computation, Volume

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.

Butz, Martin.