Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repository

Genetic Programming: On the Programming of Computers by Means of Natural Selection

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

The advent of software agents gave rise to much discussion of just what such an agent is, and of how they differ from programs in general. Here we propose a formal definition of an autonomous agent which clearly distinguishes a software agent from just any program. We also offer the beginnings of a natural kinds taxonomy of autonomous agents, and discuss possibilities for further classification. Finally, we discuss subagents and multiagent systems.

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Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents

In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

Computations are dynamical systems. The formal study of dynamical systems has revealed a spectrum of behaviors ranging from fixed-point dynamics to fully developed chaos. How does computation--especially universal computation--fit into this spectrum of dynamical behaviors?
This thesis addresses the question via the study of Cellular Automata (CA). CA are discrete space/time dynamical systems which (a) exhibit the full spectrum of dynamical behaviors, and (b) are known to support universal computation. Therefore, if we can locate the CA which support computation in the full spectrum of CA dynamics, we may learn a great deal about the general relationship between computation and dynamics.
We find that, with an appropriate parameterization of the space of CA, we can order CA dynamical behaviors from fixed-point, through periodic, to chaotic behavior. We also find that there exists a phase-transition in this sequence, located between the periodic and the chaotic rules. The dynamics in the vicinity of this phase-transition are the most complex exhibited anywhere in the spectrum, both qualitatively and quantitatively. These "complex" rules exhibit many of the properties deemed necessary to support computation, and therefore we are able to identify computation with a phase-transition in the space of CA. We are also able to explain the existence of--and relationships between--previously suggested classes of CA behavior (such as the four Wolfram classes) in terms of this phase-transition in the space of CA.
The results reported here suggest a general relationship between computation and phase-transitions, especially second-order phase-transitions. When viewed from this perspective, one is able to explain a great deal of the phenomenology of computation, such as the existence of complexity classes, computability classes, and the Halting problem. This view also leads to some new suggestions about the nature and origin of such complex natural phenomena as Life and Intelligence.

Computation at the edge of Chaos: phase transitions and emergent computation

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Computation at the edge of chaos: phase transitions and emergent computation

Swarm is a multi-agent software platform for the simulation of complex adaptive systems. In the Swarm system the basic unit of si8mulation is the swarm, a collection of agents executing a schedule of actions. Swarm supports hierarchical modeling approaches whereby agents can be composed of swarms of other agents in nested structures. Swarm provides object oriented libraries of reusable components for building models and analyzing, displaying, and controlling experiments on those models. Swarm is currently available as a beta version in full, free source code form. It requires the GNU C Compiler, Unix, and X Windows. More information about Swarm can be obtained from our web pages, [ Swarm ].

The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations

Artificial Life II

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Christopher G. Langton

Papers

Computation at the edge of Chaos: phase transitions and emergent computation

Computation at the edge of chaos: phase transitions and emergent computation

The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations

Artificial Life II

Studying artificial life with cellular automata