International Journal of Computational Intelligence and Applications

Small changes in spatial pattern on a landscape can sometimes produce dramatic ecological responses. Such transition ranges are associated with critical environmental conditions such as tree density. As the landscape becomes dissected into smaller patches of trees, landscape connectivity may suddenly become disrupted, which may have important consequences for the behaviours of forest fire, i.e. how it spreads. Landscape connectivity depends not only on the tree density but also many other environmental conditions such as land height, flammability, and wind conditions. To determine how the critical densities are affected by the changes in these conditions, we developed a fire spread simulation model using a multi-agent (i.e. bottom-up) approach. This model simulates an artificial environment where bush is randomly generated and fire can be ignited and then spread across the environment according to some "interaction rules". The emergent fire spread behaviours at the landscape level are determined by such micro-level "interaction rules". This model takes into account some major environmental factors that influence fire growth. By varying these variables under controlled conditions, this research aims to show how varied environmental conditions affect the critical density, and hence influence the spread and growth of fire.

This article describes a new strategy for analyzing contours in static images. The goal of this analysis is to find the interest centres in the images and then to explain some common optical illusion phenomena. The approach proposed is based on a modified version of the Ant Colony Optimization paradigm named Ant Scan (here introduced). Ant Scan is different from the ACO meta-heuristic principally in the mechanism of exploration of the interest space (the image to be analyzed). Experimental results have shown that interactions among these new agents (based on communication by pheromone) can generate another type of emergent phenomenon that can be utilized to explain how human beings process static images. Classical optical illusions have been analyzed with this new model and a qualitative explanation of how they act is provided.

Of all the issues discussed at Alife VII: Looking Forward, Looking Backward, the issue of whether it was possible to create an artificial life system that exhibits open-ended evolution of novelty is by far the biggest. Of the 14 open problems settled on as a result of debate at the conference, some 6 are directly, or indirectly related to this issue.

Most people equate open-ended evolution with complexity growth, although a priori these seem to be different things. In this paper I report on experiments to measure the complexity of Tierran organisms, and show the results for a size-neutral run of Tierra. In this run, no increase in organismal complexity was observed, although organism size did increase through the run. This result is discussed, offering some signposts on path to solving the issue of open ended evolution.

The purpose of this paper is to consider the effects of spatial locality on the evolution of cooperative behavior in the N-person iterated Prisoner's Dilemma (N-IPD) by focusing on two essentially distinct factors: the scale of interaction (which decides the neighboring members playing the N-person games) and the scale of reproduction (which decides the neighboring candidates for an offspring in each cell). We conducted evolutionary experiments of strategies for one-dimensional N-IPD game with various settings of these two factors. Experimental results revealed that these two factors bring qualitatively different effects to the emergence of cooperative behavior. Furthermore, we investigated the dynamics of the evolution of spatial locality in N-IPD. When we introduced the evolution of the scale of interaction into our model, the dynamic evolution of the scale of interaction through generation facilitated the emergence of global cooperation when the scale of reproduction was relatively small. Experiments with the evolution of the scale of reproduction are also discussed.

Ant colony optimization techniques are usually guided by pheromone and heuristic cost information when choosing the next element to add to a solution. However, while an individual element may be attractive, usually its long term consequences are neither known nor considered. For instance, a short link in a traveling salesman problem may be incorporated into an ant's solution, yet, as a consequence of this link, the rest of the path may be longer than if another link was chosen. The Accumulated Experience Ant Colony uses the previous experiences of the colony to guide in the choice of elements. This is in addition to the normal pheromone and heuristic costs. Two versions of the algorithm are presented, the original and an improved AEAC that makes greater use of accumulated experience. The results indicate that the original algorithm finds improved solutions on problems with less than 100 cities, while the improved algorithm finds better solutions on larger problems.

Marriage in Honey-Bees Optimization is a new swarm intelligence technique inspired by the marriage process of honey-bees. It has been shown to be very effective in solving the propositional satisfiability problem known as 3-SAT. The objective of this paper is to test a conventional annealing approach as the basis for determining the pool of drones. The modified algorithm is tested using a group of randomly generated hard 3-SAT problems to compare its behavior and efficiency against previous implementations. The overall performance of the MBO algorithm was found to have improved significantly using the proposed annealing function. Furthermore, a dramatic improvement was noted with the committee machine using this true annealing approach.