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

Cooperation is needed for evolution to construct new levels of organization. Genomes, cells, multicellular organisms, social insects, and human society are all based on cooperation. Cooperation means that selfish replicators forgo some of their reproductive potential to help one another. But natural selection implies competition and therefore opposes cooperation unless a specific mechanism is at work. Here I discuss five mechanisms for the evolution of cooperation: kin selection, direct reciprocity, indirect reciprocity, network reciprocity, and group selection. For each mechanism, a simple rule is derived that specifies whether natural selection can lead to cooperation.

/pdf/five-rules-for-the-evolution-of-cooperation-2bpocasc6i.pdf

Five Rules for the Evolution of Cooperation

Evolutionary games on graphs

Transactions of the Institute of British Geographers

For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. We propose a principle for the identification of nontriviality. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the "alphabet" used to describe those systems.

/pdf/distilling-free-form-natural-laws-from-experimental-data-7wp48fiem8.pdf

Distilling Free-Form Natural Laws from Experimental Data

A long-standing problem in biological and social sciences is to understand the conditions required for the emergence and maintenance of cooperation in evolving populations. For many situations, kin selection is an adequate explanation, although kin-recognition may still be a problem. Explanations of cooperation between non-kin include continuing interactions that provide a shadow of the future (that is, the expectation of an ongoing relationship) that can sustain reciprocity, possibly supported by mechanisms to bias interactions such as embedding the agents in a two-dimensional space or other context-preserving networks. Another explanation, indirect reciprocity, applies when benevolence to one agent increases the chance of receiving help from others. Here we use computer simulations to show that cooperation can arise when agents donate to others who are sufficiently similar to themselves in some arbitrary characteristic. Such a characteristic, or 'tag', can be a marking, display, or other observable trait. Tag-based donation can lead to the emergence of cooperation among agents who have only rudimentary ability to detect environmental signals and, unlike models of direct or indirect reciprocity, no memory of past encounters is required.

Evolution of cooperation without reciprocity.

In many domains, agent-based system modeling competes with equation-based approaches that identify system variables and evaluate or integrate sets of equations relating these variables. The distinction has been of great interest in a project that applies agent-based modeling to industrial supply networks, since virtually all computer-based modeling of such networks up to this point has used system dynamics, an approach based on ordinary differential equations (ODE’s). This paper summarizes the domain of supply networks and illustrates how they can be modeled both with agents and with equations. It summarizes the similarities and differences of these two classes of models, and develops criteria for selecting one or the other approach.

/pdf/agent-based-modeling-vs-equation-based-modeling-a-case-study-2liw8h77rh.pdf

Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide

In this paper, we identify two distinct notions of accuracy of land-use models and highlight a tension between them. A model can have predictive accuracy: its predicted land-use pattern can be highly correlated with the actual land-use pattern. A model can also have process accuracy: the process by which locations or land-use patterns are determined can be consistent with real world processes. To balance these two potentially conflicting motivations, we introduce the concept of the invariant region, i.e., the area where land-use type is almost certain, and thus path independent; and the variant region, i.e., the area where land use depends on a particular series of events, and is thus path dependent. We demonstrate our methods using an agent-based land-use model and using multitemporal land-use data collected for Washtenaw County, Michigan, USA. The results indicate that, using the methods we describe, researchers can improve their ability to communicate how well their model performs, the situations or instances in which it does not perform well, and the cases in which it is relatively unlikely to predict well because of either path dependence or stochastic uncertainty.

/pdf/path-dependence-and-the-validation-of-agent-based-spatial-510ckmmbsf.pdf

Path dependence and the validation of agent-based spatial models of land use

In many social and biological systems agents simultaneously and adaptively compete for limited resources, thereby altering their environment. We analyze a simple model that incorporates fundamental features of such systems. If the space of strategies available to the agents is small, the system is in a phase in which all information available to the agents' strategies is traded away, and agents' choices are maladaptive, resulting in a poor collective utilization of resources. For larger strategy spaces, the system is in a phase in which the agents are able to coordinate their actions to achieve a better utilization of resources. The best utilization of resources occurs at a critical point, when the dimension of the strategy space is on the order of the number of agents.

http://finance.martinsewell.com/stylized-facts/scaling/SavitManucaRiolo1999.pdf

Adaptive Competition, Market Efficiency, and Phase Transitions

The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, we identify four key relationships affecting how geographic data (fields and objects) and agent-based process models can interact: identity, causal, temporal and topological. We discuss approaches to implementing tight integration, focusing on a middleware approach that links existing GIS and ABM development platforms, and illustrate the need and approaches with example agent-based models.

/pdf/spatial-process-and-data-models-toward-integration-of-agent-3cl4g5plk9.pdf

Rick Riolo

Papers

Evolution of cooperation without reciprocity.

Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide

Path dependence and the validation of agent-based spatial models of land use

Adaptive Competition, Market Efficiency, and Phase Transitions

Spatial process and data models: Toward integration of agent-based models and GIS