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Improving Population-Based Algorithms with Fitness Deterioration

100%

Wolny A. , Schaefer R.

tom nr 4

31-44

This work presents a new hybrid approach for supporting sequential niching strategies called Cluster Supported Fitness Deterioration (CSFD). Sequential niching is one of the most promising evolutionary strategies for analyzing multimodal global optimization problems in the continuous domains embedded in the vector metric spaces. In each iteration CSFD performs the clustering of the random sample by OPTICS algorithm and then deteriorates the fitness on the area occupied by clusters. The selection pressure pushes away the next-step sample (population) from the basins of attraction of minimizers already recognized, speeding up finding the new ones. The main advantages of CSFD are low memory an computational complexity even in case of large dimensional problems and high accuracy of deterioration obtained by the flexible cluster definition delivered by OPTICS. The paper contains the broad discussion of niching strategies, detailed definition of CSFD and the series of the simple comparative tests.

Stochastic Dynamics of Evolutionary Multi-Agent Systems

100%

Byrski A. , Schaefer R.

tom z. 169

27-34

The refined model for the biologically inspired agent-based computation system EMAS conformed to BDI standard is presented. The considerations are based on the model of the system dynamics as the stationary Markov chain already presented. In the course of paper space of the system states is modified in order assure state coherency and set of actions is simplified. Such a model allows for better understanding the behavior of the proposed complex systems as well as their limitations.

Twin adaptive scheme for solving inverse problems

80%

Schaefer R. , Barabasz B. , Paszyński M.

tom z. 160

241-249

The paper deals with a class of inverse parametric problems for which the energy function may be defined. The advanced multi-deme strategy that offers an adaptive accuracy is utilized for solving associated optimal control problems. The direct problems necessary for fitness evaluation are computed by the hp-adaptive Finite Elements. The new iterative strategy balances the error of solving the direct problem and the error of solving the optimal control problem in order to descrease the total computational cost. The concept of the strategy is partially formally verified (see Lemma 3.1 and 3.2), moreover the advantages in the computational practice are mentioned.

An agent-oriented hierarchic strategy for solving inverse problems

71%

Smołka M. , Schaefer R. , Paszyński M. , Pardo D. , Álvarez-Aramberri J.

tom Vol. 25, no. 3

483--498

The paper discusses the complex, agent-oriented hierarchic memetic strategy (HMS) dedicated to solving inverse parametric problems. The strategy goes beyond the idea of two-phase global optimization algorithms. The global search performed by a tree of dependent demes is dynamically alternated with local, steepest descent searches. The strategy offers exceptionally low computational costs, mainly because the direct solver accuracy (performed by the hp-adaptive finite element method) is dynamically adjusted for each inverse search step. The computational cost is further decreased by the strategy employed for solution inter-processing and fitness deterioration. The HMS efficiency is compared with the results of a standard evolutionary technique, as well as with the multi-start strategy on benchmarks that exhibit typical inverse problems’ difficulties. Finally, an HMS application to a real-life engineering problem leading to the identification of oil deposits by inverting magnetotelluric measurements is presented. The HMS applicability to the inversion of magnetotelluric data is also mathematically verified.