Sub- Diving Labeling Method for Optimization Problem by Genetic Algorithm

In many global Optimization Problems, it is required to evaluate a global point (min or max) in large space that calculation effort is very high. In this paper is presented new approach for optimization problem with subdivision labeling method (SLM) but in this method for higher dimensional has high computational. SLM Genetic Algorithm (SLMGA) in optimization problems is one of the solutions of this problem. In proposed algorithm the initial population is crossing points and subdividing in each step is according to mutation. RSLMGA is compared with other well known algorithms: DE, PGA, Grefensstette and Eshelman and numerical results show that RSLMGA achieve global optimal point with more decision by smaller generations.Comment: arXiv admin note: substantial text overlap with arXiv:1307.5667, arXiv:1307.5679, arXiv:1307.583

GENETIC ALGORITHM-AIDED FIXED-POINT DESIGN OF E-UTRA PRACH DETECTOR ON MULTI-CORE DSP

This paper presents a new genetic algorithm (GA)-aided methodology of software implementation in Digital Signal Processors (DSPs) under both the computational accuracy and bus bandwidth constraints. The design issue is firstly stated as two classes of fixed-point design problems, one of which is then formulated to a constrained integer programming (CIP) problem. And the genetic algorithm is proposed to treat with such CIP problem for the sake of efficiency. Then the fixed-point evolved (E)-UTRA PRACH detector is presented, which further underlines the feasibility and convenience of applying this methodology to practice. Finally, the numeric results justify the proposed GA-aided approach and demonstrate that a speedup by a factor of 33 can be achieved compared to the exhaustive search for the solution of E-UTRA PRACH detector design problem. 1