no unique method has been so far specified for determining the number of neurons in hidden layers of multi-layer perceptron (mlp) neural networks used for prediction. the present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. the data used in the present research for prediction are consumption data of water...

For this thesis, different algorithms were created to solve the problem of Jop Shop Scheduling with a number of constraints. More specifically, the setting of a (simplified) car workshop was used: the algorithms had to assign all tasks of one day to the mechanics, taking into account minimum and maximum finish times of tasks and mechanics, the use of bridges, qualifications and the delivery and...

In this paper, we generalize the models used by MacKay [1] in his analysis of evolutionary strategies that are based on sexual, rather than asexual, reproduction methods. This analysis can contribute to the understanding of the relative power of genetic algorithms over search methods based upon stochastic hill-climbing, e.g. [2], [3].

The modification of message that meets the sufficient conditions for collision is found in the last step of differential attack proposed by Wang et all. (2005) on MD4 hash algorithm. Here we show how this attack phase, finding a collision starting from the list of sufficient conditions for the collision, can be implemented using a combination of two algorithms evolutionary algorithm and hill cl...

The evolutionary multiobjective optimization technique for analog circuit optimizer is presented in this paper. the technique uses a Parallel Genetic Algorithm(PGA) to identifies multiple “good” solutions from a multiobjective fitness landscape which are tuned using a local hill-climbing algorithm. The PGA is used to provide a nature niching mechanism that has considerable computational advanta...

We analyze the behavior of hill-climbing algorithms for the minimum bisection problem on instances drawn from the \planted bisection" ran-is one of the few problem distributions for which various popular heuris-tic methods, such as simulated annealing, have been proven to succeed. However, it has been open whether these sophisticated methods were necessary , or whether simpler heuristics would ...

This study empirically investigates variations of hill climbing algorithms for training artiicial neural networks on the 5-bit parity classiication task. The experiments compare the algorithms when they use diierent combinations of random number distributions, variations in the step size and changes of the neural net-works' initial weight distribution. A hill climbing algorithm which uses inlin...