Robust parameter settings of evolutionary algorithms for the optimisation of agricultural systems models

Numerical optimisation methods are being more commonly applied to agricultural systems models, to identify the most profitable management strategies. The available optimisation algorithms are reviewed and compared, with literature and our studies identifying evolutionary algorithms (including genetic algorithms) as superior in this regard to simulated annealing, tabu search, hill-climbing, and direct-search methods. Results of a complex beef property optimisation, using a real-value genetic algorithm, are presented. The relative contributions of the range of operational options and parameters of this method are discussed, and general recommendations listed to assist practitioners applying evolutionary algorithms to the solution of agricultural systems. (C) 2001 Elsevier Science Ltd. All rights reserved.

Statistical racing techniques for improved empirical evaluation of evolutionary algorithms

In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare algorithms using as many different parameter settings and test problems as possible, in border to have a clear and detailed picture of their performance. Unfortunately, the total number of experiments required may be very large, which often makes such research work computationally prohibitive. In this paper, the application of a statistical method called racing is proposed as a general-purpose tool to reduce the computational requirements of large-scale experimental studies in evolutionary algorithms. Experimental results are presented that show that racing typically requires only a small fraction of the cost of an exhaustive experimental study.

On building a principled framework for evaluating and testing evolutionary algorithms: A continuous landscape generator

In this paper, we address some issue related to evaluating and testing evolutionary algorithms. A landscape generator based on Gaussian functions is proposed for generating a variety of continuous landscapes as fitness functions. Through some initial experiments, we illustrate the usefulness of this landscape generator in testing evolutionary algorithms.

Data partitioning for parallel spatial join processing

The cost of spatial join processing can be very high because of the large sizes of spatial objects and the computation-intensive spatial operations. While parallel processing seems a natural solution to this problem, it is not clear how spatial data can be partitioned for this purpose. Various spatial data partitioning methods are examined in this paper. A framework combining the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refine strategy for spatial operation processing is proposed for parallel spatial join processing. Object duplication caused by multi-assignment in spatial data partitioning can result in extra CPU cost as well as extra communication cost. We find that the key to overcome this problem is to preserve spatial locality in task decomposition. We show in this paper that a near-optimal speedup can be achieved for parallel spatial join processing using our new algorithms.

Causality detection on US mutual fund movements using evolutionary subset time-series

In this paper we develop an evolutionary kernel-based time update algorithm to recursively estimate subset discrete lag models (including fullorder models) with a forgetting factor and a constant term, using the exactwindowed case. The algorithm applies to causality detection when the true relationship occurs with a continuous or a random delay. We then demonstrate the use of the proposed evolutionary algorithm to study the monthly mutual fund data, which come from the 'CRSP Survivor-bias free US Mutual Fund Database'. The results show that the NAV is an influential player on the international stage of global bond and stock markets.

Features of colorectal cancers with high-level microsatellite instability occurring in familial and sporadic settings

High-level microsatellite instability (AISI-H) is demonstrated in 10 to 15% of sporadic colorectal cancers and in most cancers presenting In the inherited condition hereditary nonpolyposis colorectal cancer (HNPCC). Distinction between these categories of MSI-H cancer is of clinical importance and the aim of this study was to assess clinical, pathological, and molecular features that might he discriminatory. One hundred and twelve MSI-H colorectal cancers from families fulfilling the Bethesda criteria were compared with 57 sporadic MSI-H colorectal cancers. HNPCC cancers presented at a lower age (P < 0.001) with no sporadic MSI-H cancer being diagnosed before the age of 57 years. MSI was less extensive in HNPCC cancers with 72% microsatellite markers showing band shifts compared with 87% in sporadic tumors (P < 0.001). Absent immunostaining for hMSH2 was only found in HNPCC tumors. Methylation of bMLH1 was observed in 87% of sporadic cancers but also in 55% of HNPCC tumors that showed loss of expression of hMLH1 (P = 0.02). HNPCC cancers were more frequently characterized by aberrant beta -catenin immunostaining as evidenced by nuclear positivity (P < 0.001). Aberrant p53 immunostaining was infrequent in both groups. There were no differences with respect to 5q loss of heterozygosity or codon 12 K-ras mutation, which were infrequent in both groups. Sporadic MSI-H cancers were more frequently heterogeneous (P < 0.001), poorly differentiated (P = 0.02), mucinous (P = 0.02), and proximally located (P = 0.04) than RNPCC tumors. In sporadic MSI-H cancers, contiguous adenomas were likely to be serrated whereas traditional adenomas were dominant in HNPCC. Lymphocytic infiltration was more pronounced in HNPCC but the results did not reach statistical significance. Overall, HNPCC cancers were more like common colorectal cancer in terms of morphology and expression of beta -catenin whereas sporadic MSI-H cancers displayed features consistent with a different morphogenesis. No individual feature was discriminatory for all RN-PCC cancers. However, a model based on four features was able to classify 94.5% of tumors as sporadic or HNPCC. The finding of multiple differences between sporadic and familial MSI-H colorectal cancer with respect to both genotype and phenotype is consistent with tumorigenesis through parallel evolutionary pathways and emphasizes the importance of studying the two groups separately.

Population-based continuous optimization, probabilistic modelling and mean shift

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.

A general-purpose tunable landscape generator

The research literature on metalieuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging real-world optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are made on either the algorithm itself or the problems to which it is applied, or both. As a consequence, metalieuristics are typically evaluated empirically using a set of test problems. Unfortunately, relatively little attention has been given to the development of methodologies and tools for the large-scale empirical evaluation and/or comparison of metaheuristics. In this paper, we propose a landscape (test-problem) generator that can be used to generate optimization problem instances for continuous, bound-constrained optimization problems. The landscape generator is parameterized by a small number of parameters, and the values of these parameters have a direct and intuitive interpretation in terms of the geometric features of the landscapes that they produce. An experimental space is defined over algorithms and problems, via a tuple of parameters for any specified algorithm and problem class (here determined by the landscape generator). An experiment is then clearly specified as a point in this space, in a way that is analogous to other areas of experimental algorithmics, and more generally in experimental design. Experimental results are presented, demonstrating the use of the landscape generator. In particular, we analyze some simple, continuous estimation of distribution algorithms, and gain new insights into the behavior of these algorithms using the landscape generator.