40205984
A Robust Evolutionary Algorithm for Training Neural Networks
A new evolutionary algorithm is introduced for training both feedforward and recurrent neural networks. The proposed approach, called the Family Competition Evolutionary Algorithm (FCEA), automatically achieves the balance of the solution quality and convergence speed by integrating multiple mutations, family competition and adaptive rules. We experimentally analyse the proposed approach by showing that its components can cooperate with one another, and possess good local and global properties. Following the description of implementation details, our approach is then applied to several benchmark problems, including an artificial ant problem, parity problems and a two-spiral problem. Experimental results indicate that the new approach is able to stably solve these problems, and is very competitive with the comparative evolutionary algorithms.
11778214
An Evolutionary Algorithm for Synthesizing Optical Thin-Film Designs
This paper presents an evolutionary approach, called Family Competition Evolutionary Algorithm (FCEA), to design optical thin-film multilayer systems. FCEA integrates self-adaptive mutations, decreasing-based mutations, and four-layer selections to balance exploration and exploitation. One antireflection coating and one narrow-band rejection filter are presented to demonstrate that our approach is a powerful technique. Our approach consistently performs better than other evolutionary algorithms and other published results on these two problems. From experimental results of antireflection coating, our optimal solutions exhibit a pronounced semiperiodic clustering of layers and these solutions also confirm the theoretical prediction between the optical thickness and the best achievable reflectance.
29686713
A note on Ramsey numbers with two parameters
The Ramsey number R(G1, G2) is the smallest integer p such that for any graph G on p vertices either G contains G1 or G- contains G2, where G- denotes the complement of G. In this paper, some new bounds with two parameters for the Ramsey number R(G1, G2), under some assumptions, are obtained. Especially, we prove that R(K6 - e, K6) ≤ 116 and R(K6 - e, K7) ≤ 202, these improve the two upper bounds for the classical Ramsey number in [S.P. Radziszowski, Small Ramsey number, Electron. J. Combin. DSI (2002) 1-36].
5715843
A Cautionary Tale
Recently we have been investigating various methods for improving the retrieval of documents. One frequently used context for such studies is to work with a known database and set of queries, where sufficient work has previously been done to establish an "official" set of relevant documents for each query. Working within such a context, we have discovered at least one instance wherein the combination of the document descriptions in the database, the query, and the set of documents deemed relevant foredooms our attempts at retrieval improvement to failure. This note is to caution other researchers against such possibilities, and to recommend that the database in question, that of the National Physical Laboratory (NPL), as represented on Virginia Disk 1, be retired from use.
10779366
An efficient adaptive focused crawler based on ontology learning
The enormous growth of the World Wide Web has made it important to perform resource discovery efficiently. Consequently, several new ideas have been proposed; among them a key technique is focused crawling which is able to crawl particular topical portions of the World Wide Web quickly without having to explore all Web pages. In this paper, we present an intelligent focused crawler algorithm in which we embed ontology to evaluate the page's relevance to the topic. Compared with other algorithms using domain knowledge, our algorithm can evolve the ontology automatically during crawl process. Considering the instinct characteristics of the ontology, propagation has also been imported to accelerate the evolution of the ontology. We applied our approaches in several tasks and provided an empirical evaluation which has shown promising results.
46223238
Environmental impact assessment using the evidential reasoning approach
Environmental impact assessment (EIA) problems are often characterised by a large number of identified environmental factors that are qualitative in nature and can only be assessed on the basis of human judgments, which inevitably involve various types of uncertainties such as ignorance and fuzziness. So, EIA problems need to be modelled and analysed using methods that can handle uncertainties. The evidential reasoning (ER) approach provides such a modelling framework and analysis method. In this paper the ER approach will be applied to conduct EIA analysis for the first time. The environmental impact consequences are characterized by a set of assessment grades that are assumed to be collectively exhaustive and mutually exclusive. All assessment information, quantitative or qualitative, complete or incomplete, and precise or imprecise, is modelled using a unified framework of a belief structure. The original ER approach with a recursive ER algorithm will be introduced and a new analytical ER algorithm will be investigated which provides a means for using the ER approach in decision situations where an explicit ER aggregation function is needed such as in optimisation problems. The ER approach will be used to aggregate multiple environmental factors, resulting in an aggregated distributed assessment for each alternative policy. A numerical example and its modified version are studied to illustrate the detailed implementation process of the ER approach and demonstrate its potential applications in EIA.
14693913
Feature Subset Selection Using a Genetic Algorithm
Practical pattern-classification and knowledge-discovery problems require the selection of a subset of attributes or features to represent the patterns to be classified. The authors' approach uses a genetic algorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.