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School of Computing, Information and Mathematical Sciences

Dr. Anuraganand Sharma

My research interest is mainly in Artificial Intelligence and optimization techniques in engineering specifically with the use of heuristic algorithms. Currently, I am working on image edge detection using evolutionary algorithms. I worked in the following research projects:

  • Constraint guided search through evolutionary algorithm

Many science and engineering applications require finding solutions to optimization problems by satisfying a set of constraints. These problems are typically ill-structured and intractable. They are formalized as constraint problems (CPs). Evolutionary algorithms (EAs) are known to be good solvers for optimization problems ubiquitous in various problem domains. EAs have also been used to solve CPs, however traditional EAs are ‘blind’ to constraints as they do not extract and exploit information from the constraints to better ‘inform’ search for solutions. I developed a variation of EA – Intelligent constraint handling evolutionary algorithm (ICHEA) that has been demonstrated to be a versatile constraints-guided EA for all forms of constrained problems on several benchmark problems. ICHEA works on domains with both quantitative and qualitative data and constraints. ICHEA modeling is designed to be independent of problem parameters and mostly focuses on maximally utilizing information from constraint in its search. It takes the divide and conquer approach for search through incrementally taking a sequence of constraints to solve CPs. It models constraints preferences and constraints strengths and exploits their relationships in search. The computational model developed allows for what-if analysis for exploring search spaces and for revising solution paths. Incrementality is a requisite feature as it models solving dynamic CPs where constraints arrive at run time or where constraints change over time. The challenge undertaken is to maximally utilize solutions already developed to a point in time to process any new, arriving sub-sequence of constraints rather than search for a solution anew each time a constraint changes or a new constraint arrives.

  • Clustering for Data Mining: A Hybrid Particle Swarm Optimization – Self Organizing Map Approach

Here we proposed a novel algorithm that uses Particle Swarm Optimization (PSO) algorithm to determine the cluster boundaries in the output of self-organizing map (SOM). SOM is a data mining tool that reveals structure in data sets through data visualization that is otherwise hard to detect from raw data alone. However, interpretation through visual inspection is prone to errors and can be very tedious. There are several techniques for the automatic detection of clusters of code vectors found by SOM, but they generally do not take into account the distribution of codeevectors. The results of this novel automatic method compete very favorably to boundary detection through traditional algorithms namely k-means and hierarchical based approach which are normally used to interpret the output of SOM. Particle Swarm Optimization (PSO) is one of the newly developed algorithms being investigated internationally. This kind of algorithms solves those problems where problem formulation is either impossible or very time consuming to process.

  • Inductive Modeling through GMDH and PSO

We proposed a new design methodology which is based on hybrid of particle swarm optimization (PSO) and group method of data handling (GMDH). The PSO and GMDH are two well-known nonlinear methods of mathematical modeling. This novel method constructs a GMDH network model of a population of promising PSO solutions. The new PSO-GMDH hybrid implementation is then can be applied to modeling and prediction of practical datasets.


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