Genetic Programming Theory and Practice VII (Genetic and Evolutionary Computation)

Genetic Programming Theory and Practice VII (Genetic and Evolutionary Computation)

Trent McConaghy

Language: English

Pages: 231

ISBN: 1461425018

Format: PDF / Kindle (mobi) / ePub

Genetic Programming Theory and Practice VII presents the results of the annual Genetic Programming Theory and Practice Workshop, contributed by the foremost international researchers and practitioners in the GP arena. Contributions examine the similarities and differences between theoretical and empirical results on real-world problems, and explore the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application. Application areas include chemical process control, circuit design, financial data mining and bio-informatics, to name a few. About this book: Discusses the hurdles encountered when solving large-scale, cutting-edge applications, provides in-depth presentations of the latest and most significant applications of GP and the most recent theoretical results with direct applicability to state-of-the-art problems. Genetic Programming Theory and Practice VII is suitable for researchers, practitioners and students of Genetic Programming, including industry technical staffs, technical consultants and business entrepreneurs.

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knowledge may tell us something about the problem itself. For example, let’s assume that each source of knowledge was biological in nature representing perhaps biochemical pathways, gene ontology, chromosomal location, protein-protein interactions and prior knowledge derived from microarray experiments. Preferential use of microarray knowledge may tell us that the DNA sequence variations in the Environmental Sensing of Expert Knowledge 33 best model might have something to do with gene

per class. Thus under an imbalanced binary data set in which 95% (5%) of the exemplars 48 GENETIC PROGRAMMING THEORY AND PRACTICE VII 1 1 0.9 0.95 0.8 Class-wise Detection Class-wise Detection 0.7 0.6 0.5 0.4 0.3 0.9 0.85 0.8 0.2 0.75 0.1 0 0.7 1 10 100 1000 Attribute Count (log) (a) Multifeature 1 10 100 1000 10000 Attribute Count (log) (b) Gisette Figure 3-2. Class-wide detection (Y axis) versus complexity (log of Attribute count, X axis) on the Character

one to decide what to do with suggested outliers, and he or she is the one to gain additional insights from these. The data analysis system is just an enabling technology that triggers the Symbolic Regression via Genetic Programming as a Discovery Engine 59 expert to ask a new question, and learn something new about the data-generating system. 3. Data balancing as an insightful pre-processing step and content-based outlier detection Data weighting for detecting under-represented regions of

Section 4 we present the SteadyState ALPS EA. Then, in Section 5 we describe our experimental setup for evaluating ALPS and then present our comparison on two different test problems in Sections 6 and 7. Finally, in Section 8, we close with a discussion in which we consider a variation to assigning age and give our conclusions in Section 9. 2. Premature Convergence One of the main problems with EAs is that after some time they prematurely converge on a mediocre solution and further iterations

trading activity, although it can be deceptive: it does not even reflect the overall ability of an algorithm in terms of actual profit generated (Brabazon and O’Neill, 2006). Many trades are beneficial in preventing loss during market downturns, and generate no profit at all. Thus, rather than the standard measure of percentage of profitable trades, the percentage of profitable buy trades and percentage of sell trades preventing loss for each algorithm are given in Figures 8-7 and 8-8,

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