The paper proposes an algorithm for data mining called Ant-Miner (ant-colony-based data miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts as well as principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: 1) Ant-Miner is competitive with CN2 with respect to predictive accuracy, and 2) the rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2.

The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples that nature has been an unending source of inspiration. The behaviour of bees, bacteria, glow-worms, fireflies, slime moulds, cockroaches, mosquitoes and other organisms have inspired swarm intelligence researchers to devise new optimisation algorithms. This tutorial highlights the most recent nature-based inspirations as metaphors for swarm intelligence meta-heuristics. We describe the biological behaviours from which a number of computational algorithms were developed. Also, the most recent and important applications and the main features of such meta-heuristics are reported.

The search for nature-inspired ideas, models and computational paradigms always was of great interest for computer scientists, particularly for those from the Natural Computing area. The concept of optimization is present in several natural processes as in the evolution of species, in the behavior of social groups, in the dynamics of the immune system, in the food search strategies and ecological relationships of different animal populations. This work uses the ecological concepts of habitats, ecological relationships and ecological successions to build an ecology-inspired optimization algorithm, named ECO. The proposed approach uses several populations of candidate solutions that cooperates and coevolves with each other, according to a given meta-heuristic. In this particular work, we used the Artificial Bee Colony (ABC) algorithm as the main meta-heuristic. Experiments were done for optimizing benchmarck mathematical functions. Results were compared with the ABC algorithm running without the ecology concepts previously mentioned. The ECO algorithm performed significantly better than the ABC, especially as the dimensionality of the functions increase, possibly thanks to the ecological interactions (intra and inter-habitats) that enabled the coevolution of populations. Results suggest that the eco-inspired algorithm can be an interesting alternative for numerical optimization.