After system running, the first, primary generation of the N mxindividuals is created randomly. The block diagram of the optimization process has been presented in Fig. In the presented research the proposition of the memetic genetic programming (MGP) introduction for the purpose of the analog filters design is described. A synergy of evolutionary global optimization and local optimization algorithm is called a memetic algorithm. The latter has been solved by the use of a deterministic, non-gradient local search algorithm - Hooke and Jeeves direct search method. As far as GP has been proven an effective tool of circuits networks determination, it is not as efficient in adjusting resistors, coils, or capacitors values. In the presented research both filters topology and circuit parameters values are being optimized. The use of GP and HJM is briefly presented in following paragraphs. population size, number of Monte Carlo analyses, and the like, are assumed. The process is initialized with the desired filter specifications. Finally, in Section 7, some considerations of the method future development and final conclusions are presented. Next, in Section 6, the exemplary results of an automated circuit design are placed. Section 2 explains the general algorithm of the proposed system, Sections from 3 to 5 present the descriptions of the important details of the algorithm.
The proposed design system allows for obtaining the desired frequency response and, optionally, production yield optimization. In contrary to the alternative systems, the method presented in this chapter is based on an application of a hybrid system - a synergy of genetic programming (GP) (used for the purpose of determining an optimal network of a passive filter circuit) and a deterministic local search by the means of Hooke and Jeeves method (HJM), which enables the system to find accurate values of the filter’s elements.
A wide area of solutions should be probed during the early stage of computations and its local parameters should be finally optimized. The process of circuits’ automated designing is a very complex issue. This chapter describes the passive filters synthesis method by means of EC. This property and significant computational efforts necessary for a huge generation processing predispose the EC applicability especially to the NP hard global searching problems. The main drawback of evolutionary approaches is an ineffective and insufficient local optimization. The most popular sorts of EC approaches are: genetic algorithm (GA), genetic programming (GP), evolutionary strategies (ES), differential evolution (DE) and gene expression programming (GEP). Besides, to assure a system resistance for a stagnation effect, mutation operations are applied to EC. A new generation collected after the succession procedure conserves the features consisted in the previous genotypes. During the recombination process some parts of parents’ genotypes are exchanged and offspring individuals are created. Better fitted individuals have higher survival probability and their genetic material is preferred. This kind of the optimization imitates natural processes of individuals’ competition as candidates for reproduction. Evolutionary techniques are a well known and frequently used tool of global optimization. One of the methods allowing for elimination of the mentioned problems is the use of evolutionary computations (EC).
This choice is frequently a challenge itself. Butterworth, Chebyshev) before calculating the filter transfer function’s poles and zeroes. Additionally, classical techniques of filters synthesis require assuming of the approximation type (e.g. The presence of finite load impedances for filter sections and limited quality factors of coils are just two of many concerns which a design engineer has to take into account. The design of analog passive filters with specialized (not typical) frequency responses is not a trivial problem.