ART/SOFM: A Hybrid Approach to the TSP

We present a new method of solving large scale travelling salesman problem (TSP) instances using a combination of adaptive resonance theory (ART) and self organizing feature maps (SOFM). We divide our algorithm into three phases: phase one uses ART to form clusters of cities; phase two uses a novel modification of the traditional SOFM algorithm to solve a slight variant of the TSP in each cluster of cities; and phase three uses another version of the SOFM to link all the clusters. The experimental results show that our algorithm finds approximate solutions which are about 13% longer than those reported by the chained Lin Kernighan method for problem sizes of 14,000 citie

Meta-learning computational intelligence architectures

In computational intelligence, the term \u27memetic algorithm\u27 has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a \u27meme\u27 has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as \u27memetic algorithm\u27 is too specific, and ultimately a misnomer, as much as a \u27meme\u27 is defined too generally to be of scientific use. In this dissertation the notion of memes and meta-learning is extended from a computational viewpoint and the purpose, definitions, design guidelines and architecture for effective meta-learning are explored. The background and structure of meta-learning architectures is discussed, incorporating viewpoints from psychology, sociology, computational intelligence, and engineering. The benefits and limitations of meme-based learning are demonstrated through two experimental case studies -- Meta-Learning Genetic Programming and Meta- Learning Traveling Salesman Problem Optimization. Additionally, the development and properties of several new algorithms are detailed, inspired by the previous case-studies. With applications ranging from cognitive science to machine learning, meta-learning has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning --Abstract, page iii

Evolutionary Algorithms, Markov Decision Processes, Adaptive Critic Designs, and Clustering: Commonalities, Hybridization and Performance

We briefly review and compare the mathematical formulation of Markov decision processes (MDP) and evolutionary algorithms (EA). In so doing, we observe that the adaptive critic design (ACD) approach to MDP can be viewed as a special form of EA. This leads us to pose pertinent questions about possible expansions of the methodology of ACD. This expansive view of EA is not limited to ACD. We discuss how it is possible to consider the powerful chained Lin Kernighan (chained LK) algorithm for the traveling salesman problem (TSP) as a degenerate case of EA. Finally, we review some recent TSP results, using clustering to divide-and-conquer, that provide superior speed and scalability

Application of Neuro-Fuzzy system to solve Traveling Salesman Problem

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) in solving the traveling salesman problem. Takagi-Sugeno-Kang neuro-fuzzy architecture model is used for this purpose. TSP, although, simple to describ

Reading the news through its structure: new hybrid connectivity based approaches

In this thesis a solution for the problem of identifying the structure of news published by online newspapers is presented. This problem requires new approaches and algorithms that are capable of dealing with the massive number of online publications in existence (and that will grow in the future). The fact that news documents present a high degree of interconnection makes this an interesting and hard problem to solve. The identification of the structure of the news is accomplished both by descriptive methods that expose the dimensionality of the relations between different news, and by clustering the news into topic groups. To achieve this analysis this integrated whole was studied using different perspectives and approaches. In the identification of news clusters and structure, and after a preparatory data collection phase, where several online newspapers from different parts of the globe were collected, two newspapers were chosen in particular: the Portuguese daily newspaper Público and the British newspaper The Guardian. In the first case, it was shown how information theory (namely variation of information) combined with adaptive networks was able to identify topic clusters in the news published by the Portuguese online newspaper Público. In the second case, the structure of news published by the British newspaper The Guardian is revealed through the construction of time series of news clustered by a kmeans process. After this approach an unsupervised algorithm, that filters out irrelevant news published online by taking into consideration the connectivity of the news labels entered by the journalists, was developed. This novel hybrid technique is based on Qanalysis for the construction of the filtered network followed by a clustering technique to identify the topical clusters. Presently this work uses a modularity optimisation clustering technique but this step is general enough that other hybrid approaches can be used without losing generality. A novel second order swarm intelligence algorithm based on Ant Colony Systems was developed for the travelling salesman problem that is consistently better than the traditional benchmarks. This algorithm is used to construct Hamiltonian paths over the news published using the eccentricity of the different documents as a measure of distance. This approach allows for an easy navigation between published stories that is dependent on the connectivity of the underlying structure. The results presented in this work show the importance of taking topic detection in large corpora as a multitude of relations and connectivities that are not in a static state. They also influence the way of looking at multi-dimensional ensembles, by showing that the inclusion of the high dimension connectivities gives better results to solving a particular problem as was the case in the clustering problem of the news published online.Neste trabalho resolvemos o problema da identificação da estrutura das notícias publicadas em linha por jornais e agências noticiosas. Este problema requer novas abordagens e algoritmos que sejam capazes de lidar com o número crescente de publicações em linha (e que se espera continuam a crescer no futuro). Este facto, juntamente com o elevado grau de interconexão que as notícias apresentam tornam este problema num problema interessante e de difícil resolução. A identificação da estrutura do sistema de notícias foi conseguido quer através da utilização de métodos descritivos que expõem a dimensão das relações existentes entre as diferentes notícias, quer através de algoritmos de agrupamento das mesmas em tópicos. Para atingir este objetivo foi necessário proceder a ao estudo deste sistema complexo sob diferentes perspectivas e abordagens. Após uma fase preparatória do corpo de dados, onde foram recolhidos diversos jornais publicados online optou-se por dois jornais em particular: O Público e o The Guardian. A escolha de jornais em línguas diferentes deve-se à vontade de encontrar estratégias de análise que sejam independentes do conhecimento prévio que se tem sobre estes sistemas. Numa primeira análise é empregada uma abordagem baseada em redes adaptativas e teoria de informação (nomeadamente variação de informação) para identificar tópicos noticiosos que são publicados no jornal português Público. Numa segunda abordagem analisamos a estrutura das notícias publicadas pelo jornal Britânico The Guardian através da construção de séries temporais de notícias. Estas foram seguidamente agrupadas através de um processo de k-means. Para além disso desenvolveuse um algoritmo que permite filtrar de forma não supervisionada notícias irrelevantes que apresentam baixa conectividade às restantes notícias através da utilização de Q-analysis seguida de um processo de clustering. Presentemente este método utiliza otimização de modularidade, mas a técnica é suficientemente geral para que outras abordagens híbridas possam ser utilizadas sem perda de generalidade do método. Desenvolveu-se ainda um novo algoritmo baseado em sistemas de colónias de formigas para solução do problema do caixeiro viajante que consistentemente apresenta resultados melhores que os tradicionais bancos de testes. Este algoritmo foi aplicado na construção de caminhos Hamiltonianos das notícias publicadas utilizando a excentricidade obtida a partir da conectividade do sistema estudado como medida da distância entre notícias. Esta abordagem permitiu construir um sistema de navegação entre as notícias publicadas que é dependente da conectividade observada na estrutura de notícias encontrada. Os resultados apresentados neste trabalho mostram a importância de analisar sistemas complexos na sua multitude de relações e conectividades que não são estáticas e que influenciam a forma como tradicionalmente se olha para sistema multi-dimensionais. Mostra-se que a inclusão desta dimensões extra produzem melhores resultados na resolução do problema de identificar a estrutura subjacente a este problema da publicação de notícias em linha