Pierre Collet Books

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This scarce antiquarian book is a facsimile reprint of the original. Due to its age, it may contain imperfections such as marks, notations, marginalia and flawed pages. Because we believe this work is culturally important, we have made it available as part of our commitment for protecting, preserving, and promoting the world's literature in affordable, high quality, modern editions that are true to the original work.

Main concepts of quasi-stationary distributions (QSDs) for killed processes are the focus of the present volume. The authors provide the exponential distribution property of the killing time for QSDs, present the more general result on their existence and study the process of trajectories that survive forever.

This book is devoted to the subject commonly called Chaotic Dynamics, namely the study of complicated behavior in time of maps and ? ows, called dynamical systems. The theory of chaotic dynamics has a deep impact on our understanding of - ture, and we sketch here our view on this question. The strength of this theory comes from its generality, in that it is not limited to a particular equation or scienti? c - main. It should be viewed as a conceptual framework with which one can capture properties of systems with complicated behavior. Obviously, such a general fra- work cannot describe a system down to its most intricate details, but it is a useful and important guideline on how a certain kind of complex systems may be understood and analyzed. The theory is based on a description of idealized systems, such as “hyperbolic” systems. The systems to which the theory applies should be similar to these idealized systems. They should correspond to a ? xed evolution equation, which, however, need to be neither modeled nor explicitly known in detail. Experimentally, this means that the conditions under which the experiment is performed should be as constant as possible. The same condition applies to analysis of data, which, say, come from the evolution of glaciations: One cannot apply “chaos theory” to systems under varying external conditions, but only to systems which have some self-generated chaos under ? xed external conditions.

The Evolution Artificielle cycle of conferences began as a platform for the French-speaking evolutionary computation community. Previous meetings were held in various locations in France, culminating in EA 2001 at the Université de Bourgogne in Le Creusot, an area famous for its wines. The conference has increasingly attracted international submissions, with 39 papers from non-francophonic countries across all five continents out of a total of 68. Due to the conference's single-session format, only 28 papers were presented orally, which participants appreciated. The Organizing Committee expresses gratitude to the International Program Committee for their dedication in managing the high volume of submissions and maintaining the scientific quality of the presentations. The overall quality was notably high, with all 28 presentations included in this volume, organized into eight sections that reflect the oral session's structure. An invited paper by P. Bentley highlighted his classification of interdisciplinary collaborations and showcased his work with musicians and biologists.

Iterations of continuous maps of an interval to itself serve as the simplest examples of models for dynamical systems. These models present an interesting mathematical structure going far beyond the simple equilibrium solutions one might expect. If, in addition, the dynamical system depends on an experimentally controllable parameter, there is a corresponding mathematical structure revealing a great deal about interrelations between the behavior for different parameter values. This work explains some of the early results of this theory to mathematicians and theoretical physicists, with the additional hope of stimulating experimentalists to look for more of these general phenomena of beautiful regularity, which oftentimes seem to appear near the much less understood chaotic systems. Although continuous maps of an interval to itself seem to have been first introduced to model biological systems, they can be found as models in most natural sciences as well as economics. Iterated Maps on the Interval as Dynamical Systems is a classic reference used widely by researchers and graduate students in mathematics and physics, opening up some new perspectives on the study of dynamical systems .

The content explores various applications and advancements in genetic programming (GP) and evolutionary algorithms. It includes detection of 802.11 de-authentication attacks, strategies to enhance efficiency and success rates in GP, and approaches to Solomonoff’s probabilistic induction. The text discusses a context-aware crossover operator, AQUAGP for approximate query answers, and the Blindbuilder encoding for evolving structures. It covers dynamic scheduling, emergent locomotion gaits in simulated robots, and the evolution of crossover operators for function optimization. The role of validation sets and parsimony pressure in GP is examined, along with geometric crossover for biological sequences and methods to manage constraints in evolutionary algorithms. Additionally, it highlights iterative filter generation, prototype optimization, and learning recursive functions with object-oriented GP. The concept of negative slope coefficients to characterize fitness landscapes and population clustering is also addressed. Applications in financial data projection, Sudoku solving, and the halting probability in Von Neumann architectures are discussed. The dynamics of GP are investigated through subtree crossover distance, while diversity characterization and complexity in Cartesian GP are analyzed. Lastly, it touches on robust communication systems design, developmental evaluation in GP, and optimizing decision heuristics in SAT sol