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We discuss the scenario of developing an optimizer for a given space of problem instances. Standard practice typically resorts to choosing a broad approach (such as evolutionary search), then tuning the optimizer based on example problem instances, and/or hybridizing with domain-specific heuristics and expert knowledge. This will lead to a capable optimizer for the task, but we argue that the delivered...
Epilepsy is the most common neurological disorder, affecting between 0.6% and 0.8% of the global population. During an epileptic seizure, the onset of which tends to be sudden and without prior warning, sufferers are highly vulnerable to harm, and methods that might accurately predict seizure episodes in advance are clearly of value. Building on recent work by Costa et al, we compare and contrast...
Distributed evolutionary algorithms are of increasing interest and importance for three main reasons: (i) a well designed dEA can outperform a ‘standard’ EA in terms of reliability, solution quality, and speed; (ii) they can (of course) be implemented on parallel hardware, and hence combine efficient utilization of parallel resources with very fast and reliable optimization; (iii) parallel hardware...
Algorithms for learning the structure of Bayesian Networks (BN) from data are the focus of intense research interest. Search-and-score algorithms using nature-inspired metaheuristics are an important strand of this research; however performance is variable and strongly problem-dependent. In this paper we use fitness landscape analysis to explain empirically-observed performance differences between...
Genetic programming (GP) is increasingly investigated in finance and economics. One area of study is its use to discover effective rules for technical trading in the context of a portfolio of equities (or an index). Early work in this area used GP to find rules that were profitable, but were nevertheless outperformed by the simple “buy and hold” (B&H) strategy. Attempts since then tend to report...
Learning Bayesian networks from data is an NP-hard problem with important practical applications. Several researchers have designed algorithms to overcome the computational complexity of this task. Difficult challenges remain however in reducing computation time for structure learning in networks of medium to large size and in understanding problem-dependent aspects of performance. In this paper,...
Inspired originally by the Learnable Evolution Model(LEM), we investigate LEM(ID3), a hybrid of evolutionary search with ID3 decision tree learning. LEM(ID3) involves interleaved periods of learning and evolution, adopting the decision tree construction algorithm ID3 as the learning method, and a steady state EA as the evolution component. In the learning periods, ID3 is used to infer rules that attempt...
Early and seminal work which applied evolutionary computing methods to scheduling problems from 1985 onwards laid a strong and exciting foundation for the work which has been reported over the past decade or so. A survey of the current state-of-the-art was produced in 1999 for the European Network of Excellence on Evolutionary Computing EVONET—this paper provides a more up-to-date overview of the...
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