Is swarm intelligence able to create mazes?

In this paper, the idea of applying Computational Intelligence in the process of creation board games, in particular mazes, is presented. For two different algorithms the proposed idea has been examined. The results of the experiments are shown and discussed to present advantages and disadvantages

Evolutionary improvement of programs

Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimizing its non-functional properties such as execution time, memory usage, or power consumption. In general, satisfying non-functional requirements is a difficult task and often achieved in part by optimizing compilers. However, modern compilers are in general not always able to produce semantically equivalent alternatives that optimize non-functional properties, even if such alternatives are known to exist: this is usually due to the limited local nature of such optimizations. In this paper, we discuss how best to combine and extend the existing evolutionary methods of GP, multiobjective optimization, and coevolution in order to improve existing software. Given as input the implementation of a function, we attempt to evolve a semantically equivalent version, in this case optimized to reduce execution time subject to a given probability distribution of inputs. We demonstrate that our framework is able to produce non-obvious optimizations that compilers are not yet able to generate on eight example functions. We employ a coevolved population of test cases to encourage the preservation of the function's semantics. We exploit the original program both through seeding of the population in order to focus the search, and as an oracle for testing purposes. As well as discussing the issues that arise when attempting to improve software, we employ rigorous experimental method to provide interesting and practical insights to suggest how to address these issues

The fallacy of general purpose bio-inspired computing

Bio-inspired computing comes in many flavours, inspired by biological systems from which salient features and/or organisational principles have been idealised and abstracted. These bio-inspired schemes have sometimes been demonstrated to be general purpose; able to approximate arbitrary dynamics, encode arbitrary structures, or even carry out universal computation. The generality of these abilities is typically (although often implicitly) reasoned to be an attractive and worthwhile trait. Here, it is argued that such reasoning is fallacious. Natural systems are nichiversal rather than universal, and we should expect the computational systems that they inspire to be similarly limited in their performance, even if they are ultimately capable of generality in their competence. Practical and methodological implications of this position for the use of bio-inspired computing within artificial life are outlined