Abstract not found

Abstract not found

. We propose the application of a genotype-phenotype mapping to the solution of constrained optimization problems. The method consists of strictly separating the search space of genotypes from the solution space of phenotypes. A mapping from genotypes into phenotypes provides for the appropriate expression of information represented by the genotypes. The mapping is constructed as to guarantee feasibility of phenotypic solutions for the problem under study. This enforcing of constraints causes multiple genotypes to result in one and the same phenotype. Neutral variants are therefore frequent and play an important role in maintaining genetic diversity. As a specific example, we discuss Binary Genetic Programming (BGP), a variant of Genetic Programming that uses binary strings as genotypes and program trees as phenotypes. Published in: Proceedings PARALLEL PROBLEM SOLVING FROM NATURE III Y. Davidor, H.-P. Schwefel and R. Manner (Eds.) Springer, Berlin, 1994 pp. 322 --- 332 1 Introduction...

Rule-based evolutionary online learning systems, often referred to as Michigan-style learning classifier systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generalization capabilities of genetic algorithms promising a flexible, online generalizing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with animal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in different problem types, problem structures, concept spaces, and hypothesis spaces stayed nearly unpredictable. This thesis has the following three major objectives: (1) to establish a facetwise theory approach for LCSs that promotes system analysis, understanding, and design; (2) to analyze, evaluate, and enhance the XCS classifier system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding

Four experiments explored participants' understanding of the abstract principles goincipl coinci simulatios o coulat adaptive systems. Experiments 1, 2, and 3shoBU better transfero abstract principlesacroc simulatioA that were relatively dissimilar, and that this e#ect was dueto participantswho perfocip relativelypolat o the initialsimulatioB In Experiment 4, participantsshoic better abstract understandingo asimulatio when it was depicted withcohA@CU rather than idealized graphical elements.Homents fo pom perfos.Aq/ the idealizedversio o the simulatio transferred betterto a newsimulatio gomulat by the same abstractioU The results are interpreted in termso cosAq6BP--A between abstract and codAP)U coAP)U@/A o thesimulatio)/ Individualsproi toiv coivid coividual tendto oodAPU abstractioH whenconA)C@ pro)C@qUA o superficial similarities are salient.

We propose a general-purpose method for finding high-quality solutions to hard optimization problems, inspired by self-organizing processes often found in nature. The method, called Extremal Optimization, successively eliminates extremely undesirable components of sub-optimal solutions. Drawing upon models used to simulate far-from-equilibrium dynamics, it complements approximation methods inspired by equilibrium statistical physics, such as simulated annealing. With only one adjustable parameter, its performance proves competitive with, and often superior to, more elaborate stochastic optimization procedures. We demonstrate it here on two classic hard optimization problems: graph partitioning and the traveling salesman problem. 1 In nature, highly specialized, complex structures often emerge when their most inefficient components are selectively driven to extinction. Evolution, for example, progresses by selecting against the few most poorly adapted species, rather than by expressly breeding those species best adapted to their environment [1]. To describe the dynamics of systems with

process of incremental change. Researchers have observed that software systems also exhibit characteristics of punctuation (sudden and discontinuous change) during their evolution. In this paper, we analyze punctuated evolution from the perspective of structural change. We developed a colorcoded visualization technique called the Evolution Spectrograph (ESG). ESG can be applied to highlight conspicuous changes across a historical sequence of software releases. We describe evolution spectrographs and present the empirical results from our studies of three open source software systems: OpenSSH, PostgreSQL, and Linux. We show that punctuated change occurred in the evolution of these three systems, and we validate our empirical results by examining related software documents such as change logs and release notes.

Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technology and Engineering School at George Mason University and its results are reported here. First, a general introduction to evolutionary computation is presented and recent developments in this field are briefly described. Next, the field of evolutionary design is introduced and its relevance to structural design is explained. Further, the issue of creativity/novelty is discussed and possible ways of achieving it during a structural design process are suggested. Current research progress in building engineering systems ’ representations, one of the key issues in evolutionary design, is subsequently discussed. Next, recent developments in constraint-handling methods in evolutionary optimization are reported. Further, the rapidly growing field of evolutionary multiobjective optimization is presented and briefly described. An emerging subfield of coevolutionary design is subsequently introduced and its current advancements reported. Next, a comprehensive review of the applications of evolutionary computation in structural design is provided and chronologically classified. Finally, a summary of the current research status and a discussion on the most promising paths of future research are also presented.

This research develops an evolutionary growth theory that captures the interplay between the evolution of mankind and economic growth since the emergence of the human species. This uni¯ed theory encompasses the observed evolution of population, technology and income per capita in the long transition from an epoch of Malthusian stagnation to sustained economic growth. The theory suggests that prolonged economic stagnation prior to the transition to sustained growth stimulated natural selection that shaped the evolution of the human species, whereas the evolution of the human species was the origin of the take-o ® from an epoch of stagnation to sustained growth.

Many scientific and industrial problems can be better understood by learning from samples of the task at hand. For this reason, the machine learning and statistics communities devote considerable research effort on generating inductive-learning algorithms that try to learn the true "concept" of a task from a set of its examples. Often times, however, one has additional resources readily available, but largely unused, that can improve the concept that these learning algorithms generate. These resources include available computer cycles, as well as prior knowledge describing what is currently known about the domain. Effective utilization of available computer time is important since for most domains an expert is willing to wait for weeks, or even months, if a learning system can produce an improved concept. Using prior knowledge is important since it can contain information not present in the current set of training examples. In this thesis, I present three "anytime" approaches to connec...