Earthquakes frequently destroy the homes and livelihoods of thousands. One of the most important concerns after an earthquake is to find a safe shelter for the affected people. Because of large numbers of potential locations, the multitude of constraints (e.g. access to infrastructures; security); and the uncertainty prevailing (e.g., number of places required) the identification of optimal she...

We propose a Grammatical Evolution approach to the automatic design of Ant Colony Optimization algorithms. The grammar adopted by this framework has the ability to guide the learning of novel architectures, by rearranging components regularly found on human designed variants. Results obtained with several TSP instances show that the evolved algorithmic strategies are effective, exhibit a good g...

(2008) Flexible protein folding by ant colony optimization.

Article history: Received 2 March 2014 Received in revised form 13 May 2014 Accepted 28 May 2014 Available online 23 June 2014

In this paper, an effective ant colony algorithm is proposed for solving the graph planarization problem. In the proposed algorithm, two kinds of pheromone are adopted to reinforce the search ability, and each kind of pheromone consists of two elements. The proposed algorithm is verified by a large number of simulation runs and compared with other algorithms. The experiment results show that th...

Ant-based algorithms are successful tools for solving complex problems. One of these problems is the Linear Ordering Problem (LOP). The paper shows new results on some LOP instances, using Ant Colony System (ACS) and the StepBack Sensitive Ant Model (SB-SAM).

This paper proposes an algorithm for data mining called Ant-Miner (ant-colony-based data miner). The goal of AntMiner 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 classificat...

C metric boxplots and real values Fig. 1. C metric values represented by means of boxplots comparing the different algorithms .

Published as a chapter in Approximation Algorithms and Metaheuristics, a book edited by T. F. Gonzalez.