The goal of getting computers to automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what Turing called “machine intelligence ” [161, 162].

Abstract. 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 unconstrained 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.

breve is a 3D simulation environment designed for simulation of decentralized systems and artificial life. While

A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

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 Push’s 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.

We report how breve, a simulation environment with rich 3d graphics, was used to discover significant patterns in the dynamics of a system that evolves controllers for swarms of goal-directed agents. These patterns were discovered via visualization in the sense that we had not considered their relevance or thought to look for them initially, but they became obvious upon visually observing the behavior of the system.

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.

This paper discusses the evolution of diversifying reproduction.

... This paper raises issues resulting from attempts at extending standard GP to Turing Complete representations. Firstly, there is a problem when a contiguous peice of code is moved to a new location (in a different program) by crossover. In general its functionality will be altered if global memory is used, as other parts of the program may access the same peice of memory. Secondly, traditional crossover does not respect modules. Crossover can disrupt a group of instructions that were working together (e.g. in the body of a loop) in one parent, but end up separated in two different offspring after reproduction. A crossover

In nature, systems with enormous numbers of components (i.e. cells) are evolved from a relatively small genotype. It has not yet been demonstrated that artificial evolution is sufficient to make such a system evolvable. Consequently researchers have been investigating forms of computational development that may allow more evolvable systems. The approaches taken have largely used re-writing, multi- cellularity, or genetic regulation. In many cases it has been difficult to produce general purpose computation from such systems. In this paper we introduce computational development using a form of Cartesian Genetic Programming that includes self-modification operations. One advantage of this approach is that ab initio the system can be used to solve computational problems. We present results on a number of problems and demonstrate the characteristics and advantages that self-modification brings.