This work describes a method to control a behaviour of intelligent data mining agent. We developed an adaptive decision making system that utilizes genetic programming technique to evolve an agent’s decision strategy. The parameters of data mining task and current state of an agent are taken into account by tree structures evolved by genetic programming. Efficiency of decision strategies is com...

In this paper multi-objective evolutionary algorithm is proposed to design a universal electric motor. Designing a product often incorporates multiple objectives. Designing a product family has an added tradeoff, between commonality and individual product performance. The presence of multiple objectives gives rise to a set of Pareto-optimal solutions for individual products as well as the produ...

Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving arbitrary real world problems there are some salient issues which require further investigation. One of these issues is how a uniform distribution of solutions along the Pareto non-dominated front can be obtained for badly scaled objective functions. This is especially a problem if the bounds for ...

The problem of feature selection in data mining is an important real-world problem that involves multiple objectives to be simultaneously optimized. In order to tackle this problem this work proposes a multiobjective genetic algorithm for feature selection based on the wrapper approach. The algorithm’s main goal is to find the best subset of features that minimizes both the error rate and the s...

Wavelet performances differ from one application to another and from one database to another. In this case, one can try to find out for each application the appropriate wavelet transform which results in better performances and consumes minimal resources once implemented on an FPGA platform. Accordingly, we use a generic lifting wavelet transform with p0 and q parameters. Thus, we train the opt...

Vehicle driving consumes time and energy (fuel, electricity etc.). Usually both have to be minimized. Minimizing the consumption of one of them leads to increasing the consumption of the other. To find driving strategies that take into consideration both objectives, we have implemented a multiobjective genetic algorithm that constructs driving strategies as sets of rules. Optimal sets of rules ...

Evolutionary computations are emerging as powerful tools for search and optimisation, and increasingly being used in many scientific and engineering applications. Side-by-side objectoriented computing has revolutionised, during the current decade, the style of programming and the software system design and development which is now configured around ‘class’ concept. In this paper, we present a g...

This paper presents a new approach to multiobjective optimization by evolutionary algorithm. The approach is based on fuzzification of Pareto dominance relation. Using fuzzy degrees of dominance, a set of vectors (multiple objectives) can be partially ranked. The FDD algorithm, a modification of standard genetic algorithm using this ranking scheme for the selection operations, is presented and ...

In optimization, multiple objectives and constraints cannot be handled independently of the underlying optimizer. Requirements such as continuity and di erentiability of the cost surface add yet another con icting element to the decision process. While \better" solutions should be rated higher than \worse" ones, the resulting cost landscape must also comply with such requirements. Evolutionary ...

In this paper, we present a hardware-software cosynthesis system, called MOGAC, that partitions and schedules embedded system specifications consisting of multiple periodic task graphs. MOGAC synthesizes real-time heterogeneous distributed architectures using an adaptive multiobjective genetic algorithm that can escape local minima. Price and power consumption are optimized while hard real-time...