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As per available reports about 6 relevant journals , 4 Conferences , 2 workshops are presently dedicated exclusively to differential evolution about 29 articles are being published on differential evolution.
Differential evolution is a method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Such methods are commonly known as metaheuristics as they make few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. However, metaheuristics such as DE do not guarantee an optimal solution is ever found.
DE is used for multidimensional real-valued functions but does not use the gradient of the problem being optimized, which means DE does not require for the optimization problem to be differentiable as is required by classic optimization methods such as gradient descent and quasi-newton methods. DE can therefore also be used on optimization problems that are not even continuous, are noisy, change over time, etc. DE optimizes a problem by maintaining a population of candidate solutions and creating new candidate solutions by combining existing ones according to its simple formulae, and then keeping whichever candidate solution has the best score or fitness on the optimization problem at hand. In this way the optimization problem is treated as a black box that merely provides a measure of quality given a candidate solution and the gradient is therefore not needed.
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Scope and Importance:
A new approach based on Differential Evolution (DE) for the automatic classification of items in medical databases is proposed. Based on it, a tool called DEREx is presented, which automatically extracts explicit knowledge from the database under the form of IF-THEN rules containing AND-connected clauses on the database variables. Each DE individual codes for a set of rules. For each class more than one rule can be contained in the individual, and these rules can be seen as logically connected in OR. Furthermore, all the classifying rules for all the classes are found all at once in one step. DEREx is thought as a useful support to decision making whenever explanations on why an item is assigned to a given class should be provided, as it is the case for diagnosis in the medical domain. The major contribution of this paper is that DEREx is the first classification tool in literature that is based on DE and automatically extracts sets of IF-THEN rules without the intervention of any other mechanism. In fact, all other classification tools based on DE existing in literature either simply find centroids for the classes rather than extracting rules, or are hybrid systems in which DE simply optimizes some parameters whereas the classification capabilities are provided by other mechanisms. For the experiments eight databases from the medical domain have been considered. First, among ten classical DE variants, the most effective of them in terms of highest classification accuracy in a ten-fold cross-validation has been found. Secondly, the tool has been compared over the same eight databases against a set of fifteen classifiers widely used in literature. The results have proven the effectiveness of the proposed approach, since DEREx turns out to be the best performing tool in terms of highest classification accuracy. Also statistical analysis has confirmed that DEREx is the best classifier. When compared to the other rule-based classification tools here used, DEREx needs the lowest average number of rules to face a problem, and the average number of clauses per rule is not very high. In conclusion, the tool here presented is preferable to the other classifiers because it shows good classification accuracy, automatically extracts knowledge, and provides users with it under an easily comprehensible form.
2. 5th Biodiversity Conference, March 10-12, 2016 Madrid, Spain
3. Molecular Biology Conference, October 13-15, 2016, Dubai, UAE
5. 12th Biotechnology Congress, November 14-15, 2016, San Francisco, USA
6. 11th Euro Biotechnology Congress, November 7-9, 2016, Alicante, Spain
7. Human Genetics Congress, October 31- November 02, 2016, Valencia, Spain
10. IEEE Congress on Evolutionary Computation
11. The Generation & Evolutionary Computation Conference
12. 9th International Meeting On Computational Intelligence Methods For Bioinformatics and Biostatistics
13. Annual meeting of the ISCE, Iguazu Falls, Brazil
14. Evolution 2015, Guarujá, Brazil
15. Gordon Research Conference: Molecular Mechanisms in Evolution, Easton, MA
16. M2M Evolution, Las Vegas, Nevada
17. Plant Genome Evolution 2015, Amsterdam, Netherlands
18. HBES 2016, Vancouver, Canada.
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This page was last updated on 12th Sep, 2015
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