Artificial Life 

About: Artificial Life is an academic journal published by The MIT Press. The journal publishes majorly in the area(s): Artificial life & Population. It has an ISSN identifier of 1064-5462. Over the lifetime, 1496 publications have been published receiving 51630 citations.

An Introduction to Genetic Algorithms.

Abstract: An Introduction to Genetic Algorithms is one of the rare examples of a book in which every single page is worth reading. The author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues, yet the book is concise (200 pages) and readable. Although Mitchell explicitly states that her aim is not a complete survey, the essentials of genetic algorithms (GAs) are contained: theory and practice, problem solving and scientific models, a \"Brief History\" and \"Future Directions.\" Her book is both an introduction for novices interested in GAs and a collection of recent research, including hot topics such as coevolution (interspecies and intraspecies), diploidy and dominance, encapsulation, hierarchical regulation, adaptive encoding, interactions of learning and evolution, self-adapting GAs, and more. Nevertheless, the book focused more on machine learning, artificial life, and modeling evolution than on optimization and engineering.

Ant algorithms for discrete optimization

Abstract: This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

Hidden Order: How Adaptation Builds Complexity.

Abstract: Computer simulations of artificial ecologies typically model the interactions of a population of independently acting, spatially situated, resource-restricted, differently structured, self-reproducing agents. The interactions in such systems are typically highly nonlinear, subject to random perturbation, executed in parallel, and coevolutionary in the sense that the agents interact with each other as well as their environment. Examples of artificial ecologies that possess the above characteristics include Skipper's \"computer zoo\" of migrating flocks of hierarchically invokable fragments of assembly code [10], Shanahan's populations of evolutionary automata [9], Lindgren's coevolving populations of strategies for playing the iterated prisoner's dilemma game [5], the Turing gas of self-organizing groups of autocatalytic algorithmic fragments of Rasmussen, KnudLsen, and Feldberg [6], Ray's populations of parasitic and symbiotic self-reproducing assembly code programs [8], the populations of redcode coreworld creatures of Rasmussen, Knudsen, Feldberg, and Hindsholm [7], Werner and Dyer's populations of males and females that coevolve communication [11], and Ackley and Littman's artificial world for testing the Baldwin effect concerning learning and evolution [1]. Each of these complex adaptive systems (and many others like them) succeed in illustrating one or more key features of living systems. However, each of these systems live up to their name in the worst possible way—they are all exceedingly complex. The emergent behavior and other interesting phenomena are obscured by so much modelspecific detail that it is rarely clear whether the observed phenomena represent any important general principles or are merely artifacts of the details. Moreover, all of these existing systems are computationally expensive and deliver little in the way of important emergent phenomena in relation to the amount of computational effort expended. This excessive overhead is not merely a matter of inefficiency or inconvenience; it may actually preclude emergence of important phenomena that can only materialize in the presence of certain minimum amounts of time or matter. John Holland's new book, Hidden Order: How Adaptation Builds Complexity [3],

Genetic Programming II: Automatic Discovery of Reusable Programs.

Abstract: Reading Genetic Programming IE Automatic Discovery ofReusable Programs (GPII) in its entirety is not a task for the weak-willed because the book without appendices is about 650 pages. An entire previous book by the same author [1] is devoted to describing Genetic Programming (GP), while this book is a sequel extolling an extension called Automatically Defined Functions (ADFs). The author, John R. Koza, argues that ADFs can be used in conjunction with GP to improve its efficacy on large problems. "An automatically defined function (ADF) is a function (i.e., subroutine, procedure, module) that is dynamically evolved during a run of genetic programming and which may be called by a calling program (e.g., a main program) that is simultaneously being evolved" (p. 1). Dr. Koza recommends adding the ADF technique to the "GP toolkit." The book presents evidence that it is possible to interpret GP with ADFs as performing either a top-down process of problem decomposition or a bottom-up process of representational change to exploit identified regularities. This is stated as Main Point 1. Main Point 2 states that ADFs work by exploiting inherent regularities, symmetries, patterns, modularities, and homogeneities within a problem, though perhaps in ways that are very different from the style of programmers. Main Points 3 to 7 are appropriately qualified statements to the effect that, with a variety of problems, ADFs pay off be-

Evolving 3d morphology and behavior by competition

Abstract: This article describes a system for the evolution and coevolution of virtual creatures that compete in physically simulated three-dimensional worlds. Pairs of individuals enter one-on-one contests in which they contend to gain control of a common resource. The winners receive higher relative fitness scores allowing them to survive and reproduce. Realistic dynamics simulation including gravity, collisions, and friction, restricts the actions to physically plausible behaviors. The morphology of these creatures and the neural systems for controlling their muscle forces are both genetically determined, and the morphology and behavior can adapt to each other as they evolve simultaneously. The genotypes are structured as directed graphs of nodes and connections, and they can efficiently but flexibly describe instructions for the development of creatures' bodies and control systems with repeating or recursive components. When simulated evolutions are performed with populations of competing creatures, interesting and diverse strategies and counterstrategies emerge.