Recent studies on a floating building block representation for the genetic algorithm (GA) suggest that there are many advantages to using the floating representation. This paper investigates the behavior of the GA on floating representation problems in response to three different types of pressures: (1) a reduction in the amount of genetic material available to the GA during the problem solving...

A genetic algorithm (GA) uses the principles of evolution, natural selection, and genetics to offer a method for parallel search of complex spaces. Motivated by past work using the GA as a tool for off-line computer-aided-design of control systems, we introduce several ways to use the GA for on-line identification and controller tuning. In particular, we introduce two new, but closely related a...

Recent studies on a oating building block representation for the genetic algorithm (GA) suggest that there are many advantages to using the oating representation. This paper investigates the behavior of the GA on oating representation problems in response to three diierent types of pressures: (1) a reduction in the amount of genetic material available to the GA during the problem solving proces...

Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within the framework of competitive learning. This new perspective reveals a number of different possibili...

This paper proposes an algorithm for Public Key Cryptography (PKC) using the hybrid concept of two evolutionary algorithms, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) respectively. PSO alone are fast and easy to implement, they follow the procedures of common evolutionary algorithm and posses memory feature which is absent in GA making it more valuable. In GA whole population ...

In this paper a two-dimensional non-oriented guillotine bin packing problem is studied when items have different priorities. Our objective is to maximize the total profit which is total revenues minus costs of used bins and wasted area. A genetic algorithm is developed to solve this problem where a new coding scheme is introduced. To evaluate the performance of the proposed GA, first an upper b...

The array factor (sidelobe level, SLL) of a linear array is optimized using modified continuous genetic algorithms in this work. The amplitudes and phases of the currents as well as the separation of the antennas are all taken as variables to be controlled. The results of the design using modified GA versions are compared with other methods. Two design problems were studied using several contin...

This paper addresses application mapping technique targeted for large-scale Network-on-chip (NoC). The increasing number of intellectual property (IP) cores in multi-processor System-on-Chips (MPSoCs) makes NoC application mapping more challenging to find optimum core-to-topology mapping. The factorial increase in possible mappings space requires a mapping algorithm to efficiently look for pote...