Jon Klein

Emergence of Collective Behavior in Evolving Populations of Flying Agents

We demonstrate the emergence of collective behavior in two evolutionary computation systems, one an evolutionary extension of a classic (highly constrained) flocking algorithm and the other a relatively un-constrained system in which the behavior of agents is governed by evolved computer programs. The first system demonstrates the evolution of a form of multicellular organization, while the second demonstrates the evolution of a form of altruistic food sharing. In this article we describe both systems in detail, document the emergence of collective behavior, and argue that these systems present new opportunities for the study of group dynamics in an evolutionary context. We also provide a brief overview of the breve simulation environment in which the systems were produced, and of breve’s facilities for the rapid, exploratory development of visualization strategies for artificial life.

The Push3 execution stack and the evolution of control

The Push programming language was developed for use in genetic and evolutionary computation systems, as the representation within which evolving programs are expressed. It has been used in the production of several significant results, including results that were awarded a gold medal in the Human Competitive Results competition at GECCO-2004. One of Pushs attractive features in this context is its transparent support for the expression and evolution of modular architectures and complex control structures, achieved through explicit code self-manipulation. The latest version of Push, Push3, enhances this feature by permitting explicit manipulation of an execution stack that contains the expressions that are queued for execution in the interpreter. This paper provides a brief introduction to Push and to execution stack manipulation in Push3. It then presents a series of examples in which Push3 was used with a simple genetic programming system (PushGP) to evolve programs with non-trivial control structures.

Emergence of collective behavior in evolving populations of flying agents

We demonstrate the emergence of collective behavior in two evolutionary computation systems, one an evolutionary extension of a classic (highly constrained) flocking algorithm and the other a relatively un-constrained system in which the behavior of agents is governed by evolved computer programs. We describe the systems in detail, document the emergence of collective behavior, and argue that these systems present new opportunities for the study of group dynamics in an evolutionary context.

Unwitting distributed genetic programming via asynchronous JavaScript and XML

The success of a genetic programming system in solving a problem is often a function of the available computational resources. For many problems, the larger the population size and the longer the genetic programming run the more likely the system is to find a solution. In order to increase the probability of success on difficult problems, designers and users of genetic programming systems often desire access to distributed computation, either locally or across the internet, to evaluate fitness cases more quickly. Most systems for internet-scale distributed computation require a users explicit participation and the installation of client side software. We present a proof-of-concept system for distributed computation of genetic programming via asynchronous javascript and XML (AJAX) techniques which requires no explicit user interaction and no installation of client side software. Clients automatically and possibly even unknowingly participate in a distributed genetic programming system simply by visiting a webpage, thereby allowing for the solution of genetic programming problems without running a single local fitness evaluation. The system can be easily introduced into existing webpages to exploit unused client-side computation for the solution of genetic programming and other problems.

Trivial Geography in Genetic Programming

Geographical distribution is widely held to be a major determinant of evolutionary dynamics. Correspondingly, genetic programming theorists and practitioners have long developed, used, and studied systems in which populations are structured in quasi-geographical ways. Here we show that a remarkably simple version of this idea produces surprisingly dramatic improvements in problem-solving performance on a suite of test problems. The scheme is trivial to implement, in some cases involving little more than the addition of a modulus operation in the population access function, and yet it provides significant benefits on all of our test problems (ten symbolic regression problems and a quantum computing problem). We recommend the broader adoption of this form of “trivial geography” in genetic programming systems.

Genetic Stability and Territorial Structure Facilitate the Evolution of Tag-Mediated Altruism

Evolutionary theorists have long been interested in the conditions that permit the evolution of altruistic cooperation. Recent work has demonstrated that altruistic donation can evolve in surprisingly simple models, in which agents base their decisions to donate solely on the similarity of evolved tags relative to evolved tag-difference tolerances. There is disagreement, however, about the conditions under which tag-mediated altruism will in fact evolve. Here we vary two critical parameters in a standard model of tag-mediated altruismgenetic stability and territorial structureand show that altruism evolves in a wide range of conditions. We demonstrate the evolution of significant levels of altruism even when the immediate costs to donors equal the benefits to recipients. We describe the mechanism that permits the emergence of altruism in the model as a form of kin selection that is facilitated by interactions between altruism, genetic drift, and fecundity.

Division blocks and the open-ended evolution of development, form, and behavior

We present a new framework for artificial life involving physically simulated, three-dimensional blocks called Division Blocks. Division Blocks can grow and shrink, divide and form joints, exert forces on joints, and exchange resources. They are controlled by recurrent neural networks that evolve, along with the blocks, by natural selection. Division Blocks are simulated in an environment in which energy is approximately conserved, and in which all energy derives ultimately from a simulated sun via photosynthesis. In this paper we describe our implementation of Division Blocks and some of the ways that it can support experiments on the open-ended evolution of development, form, and behavior. We also present preliminary data from simulations, demonstrating the reliable emergence of cooperative resource transactions.

Genetic Programming with Historically Assessed Hardness

We present a variation of the genetic programming algorithm, called Historically Assessed Hardness (HAH), in which the fitness rewards for particular test cases are scaled in proportion to their relative difficulty as gauged by historical solution rates. The method is similar in many respects to some realizations of techniques such as implicit fitness sharing, stepwise adaptation of weights and fitness case selection, but the details differ and HAH is generally simpler and more efficient. It also leads to different generalizations. We present results from large-scale, systematic tests of HAH and we also discuss the technique in terms of the alternative explanations that it supports for the efficacy of implicit fitness sharing and related methods.

Machine invention of quantum computing circuits by means of genetic programming

Abstract We demonstrate the use of genetic programming in the automatic invention of quantum computing circuits that solve problems of potential theoretical and practical significance. We outline a developmental genetic programming scheme for such applications; in this scheme the evolved programs, when executed, build quantum circuits and the resulting quantum circuits are then tested for “fitness” using a quantum computer simulator. Using the PushGP genetic programming system and the QGAME quantum computer simulator we demonstrate the invention of a new, better than classical quantum circuit for the two-oracle AND/OR problem.

Multidimensional Tags, Cooperative Populations, and Genetic Programming

We present new results on the evolution of tag-mediated cooperation, demonstrating that the use of multidimensional tags can enhance the emergence of high levels of cooperation. We discuss these results in the context of prior cases in which work on the evolution of cooperation has led to practical techniques for improving the problem-solving performance of genetic programming systems.