Programming and writing about it.

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Prof. Deb’s podcast on Science Watch

Hear Prof. Kalyanmoy Deb speak about his research on Multi-objective Optimization, Evolutionary Algorithms, Innovisation, Multi-modal Optimization in this ScienceWatch podcast:

Kalyanmoy Deb\’s podcast


SEAL 2010 Papers I liked reading

SEAL 2010 is happening at IIT Kanpur, India from the 1st to 4th December, 2010. As someone who was a part of the initial arrangements and was part of the lab hosting it, it is surely going to be legendary what with people like Narendra Karmarkar delivering keynote lectures. Needless to say, it would have been great to be present.

The papers are up on the Springer website and here are some papers I liked reading:

  • Improving Differential Evolution by Altering Steps in EC: This is a very approachable paper where the authors describe their experiments by modifying a standard DE algorithm my incorporating relevant ideas from another EA, G3-PCX. The bigger picture is to move towards unified approach to Evolutionary Computing
  • Bayesian Reliability Analysis under Incomplete Information Using Evolutionary Algorithms
  • Metamodels for Fast Multi-objective Optimization: Trading off Global Exploration and Local Exploitation
  • Generating Sequential Space-Filling Designs Using Genetic Algorithms and Monte Carlo Methods
  • And a paper which I would have surely liked, had I udnerstood the paper fully would be Beyond Convexity: New Perspectives in Computational Optimization

The papers are available online at


A Bi-criterion Approach to Multimodal Optimization: Self-adaptive Approach


In a multimodal optimization task, the main purpose is to find multiple optimal solutions, so that the user can have a better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be replaced by another optimum solution. Recently, we proposed a novel and successful evolutionary multi-objective approach to multimodal optimization. Our work however made use of three different parameters which had to be set properly for the optimal performance of the proposed algorithm. In this paper, we have eliminated one of the parameters and made the other two self-adaptive. This makes the proposed multimodal optimization procedure devoid of user specified parameters (other than the parameters required for the evolutionary algorithm). We present successful results on a number of different multimodal optimization problems of upto 16 variables to demonstrate the generic applicability of the proposed algorithm.

The full paper is available from Springer’s website here