Research and Development in Intelligent Systems XXIII: by Arjen Hommersom, Perry Groot, Peter Lucas, Michael Balser,

By Arjen Hommersom, Perry Groot, Peter Lucas, Michael Balser, Jonathan Schmitt (auth.), Professor Max Bramer BSc, PhD, CEng, FBCS, FIEE, FRSA, Dr Frans Coenen PhD, Dr Andrew Tuson MA, MSc, PhD, MBCS (eds.)

RESEARCH AND improvement IN clever platforms XXIII

The papers during this quantity are the refereed technical papers provided at AI-2006, the Twenty-sixth SGAI foreign convention on cutting edge suggestions and functions of man-made Intelligence, held in Cambridge in December 2006.

They current new and cutting edge advancements within the box, divided into sections on AI options, wisdom Discovery in facts, Argumentation, discussion video games and Optimisation, wisdom illustration and administration, Semantic net, and version dependent structures and Simulation. For the 1st time the quantity additionally comprises the textual content of brief papers awarded as posters on the conference.

This is the twenty-third quantity within the examine and improvement sequence. The sequence is key interpreting when you desire to sustain to this point with advancements during this very important field.

The program circulation papers are released as a spouse quantity lower than the identify functions and techniques in clever platforms XIV.

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Extra resources for Research and Development in Intelligent Systems XXIII: Proceedings of AI-2006, the Twenty-sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence

Sample text

In fact, the IG_RSLs and DPSOLs algorithms generated the best results among 16 algorithms compared. In addition, a new PTL crossover operator is developed. The PTL crossover generates two unique offspring even from the identical two parents. The advantage of the PTL operator is owing to the fact that when the population converges, the individuals in the population 29 Table 6. 44 Mean resemble each other and parents chosen from the converged population generates almost identical child as their parents.

This is a very important feature because, according to recent research results, we have experimentally shown that the initialization strategy is very important in GGGP. The overall results ofGGGP are strongly affected by the initialization method used. The results section reported that GBIM can lead the GGGP system employed to the highest convergence speed with an intermediate computational cost. We suggest that the high convergence speed is because GBIM provides an adequate size distribution of the individuals in the initial population, which creates more structural diversity.

The paper is concluded in the final section along with short discussion on further research. 2. The Proposed method The following iterative algorithm has been proposed for changing the gradient based search direction using a gain value. Initialise the initial weight vector with random values and the vector ofgain values with unit values. Repeat the following steps J and 2 on an epoch-by-epoch basis until the given error minimisation criteria are satisfied. Step 1 By introducing gain value into activation function, calculate the gradient oferror with respect to weights by using Equation (5), and gradient oferror with respect to the gain parameter by using Equation (7) Step 2 Use the gradient weight vector and gradient ofgain vector calculated in step J to calculate the new weight vector and vector ofnew gain values for use in the next epoch.

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