Project Description:

We are studying the use of evolutionary multiobjective optimization (EMOO) techniques, with emphasis on the following issues:

• Development of new techniques using concepts of mathematical programming

• Analysis and organization of current bibliography on the area

• Development of software for EMOO techniques and comparisons among different approaches

• Applications of EMOO techniques

Research Opportunities:

We do not have an specific project associated with this area at this moment, but we have a lot of interest in collaborating and/or advising any student interested in this topic. Anyone interested in this area may collaborate in one or more of the activities mentioned above and/or propose related projects.

Design of Combinational Circuits

Project Description:

We are exploring the use of different GA representations and techniques that allow us to automate the design of logic combinational circuits defined by a truth table. The output to the problem is a Boolean expression representing a fully functional circuit which is optimal in a certain quantitative way (e.g., the number of gates used).

Research Opportunities:

Anyone interested in this project may collaborate in one or more of the following activities:

• Study and develop tools to explore the fitness landscapes of different combinational circuits, in an attemp to identify the types of circuits that are particularly difficult for a GA. This would also facilitate the development of tools that could improve the performance of the GA in such problems (PhD student required).

• Develop an implementation of the system with a friendly (graphical) user interface. The software should run under Windows. (Undergraduate student required)

• Explore alternative representation schemes for this problem, looking for a trade-off between the variable-length linear representation currently used and the tree representation used in genetic programming. (Masters student required)

• Explore the use of alternative operators and study the effect of speciation and parameter fine-tuning in the solution process. Study the use of self-adaptation techniques. Use case-based reasoning techniques to extract patterns of design from small circuits to reuse them in larger and more complex circuits.

• Analyze the effect of the representation and the degree of deception of the problem (dependent upon the representation scheme used). (PhD student required)

Constraint-Handling for Numerical Optimization

Proyect Description:

We are studying the use of techniques that make unnecessary the use of the traditional penalty factors to handle constraints in an optimization problem. There are 3 main paths of research that we are interested to explore:

• Use of techniques based in the immune system

• Use of self-adaptive penalties

• Use of multiobjective optimization techniques

Research Opportunities:

Anyone interested in this project may collaborate in one or more of the following activities:

• Implementing the existing techniques for constraint-handling in the context of numerical optimization.

• All of them must be implemented in the same platform to allow a fair comparison. The resulting system will be made public domain. (Undergraduate student required)

• Develop a set of test functions that include: linear and non-linear constraints, equality and inequality constraints, discrete and continuous variables, search spaces of low and high dimensionality, disjoint feasible regions, non-convex search spaces, small feasible regions within a large search space, real-world problems where constraints are generated by a (black box) module and can not be defined in algebraic form. (Masters student required)

• Define a set of performance measures that allow a fair comparison of the different constraint-handling techniques under study. We aim at measuring not only the number of fitness function evaluations (the typical measure of performance), but also certain aspects of convergence, such as the way and speed at which a technique approaches the feasible region and its robustness. (Masters student required)

• Evaluation of the existing techniques using the platform developed in the first point and the test functions defined in the second. The performance measure under study will be those from the third point. (Masters student required)

• Development of new constraint-handling techniques based on the experience obtained from the experiments performed. The paths of research of main interest are those mentioned above. (Masters student required)