Digital circuit design through simulated evolution (SimE)
نویسندگان
چکیده
AbstractIn this paper, the use of Simulated Evolution (SimE) Algorithm in the design of digital logic circuits is proposed. SimE algorithm consists of three steps: evaluation, selection and allocation. Two goodness measures are designed to guide the selection and allocation operations of SimE. Area, power and delay are considered in the optimization of circuits. Results obtained by SimE algorithm are compared to those obtained by Genetic Algorithm (GA).
منابع مشابه
Fuzzified Simulated Evolution Algorithm for Combinational Digital Logic Design Targeting Multi-objective Optimization
In this paper, we employ fuzzified Simulated Evolution (SimE) algorithm for combinational digital logic design targeting area, delay and power as objectives. This technique is considered to be an evolutionary technique in logic design compared to conventional technique which uses deterministic algorithms for logic design. The performance of the proposed algorithm is evaluated using selected ISC...
متن کاملFuzzified Simulated Evolution Algorithm for Multi-objective Optimization of Combinational Logic Circuits
In this paper, we employ fuzzified Simulated Evolution (SimE) Algorithm for combinational logic design. SimE algorithm consists of three steps: evaluation, selection and allocation. Multilevel Logic Based Goodness measure is designed to guide the selection and allocation operations of SimE. Area, power and delay are considered in the optimization of circuits. The performance of the proposed alg...
متن کاملComputing Optimized Curves with NURBS Using Evolutionary Intelligence
In curve fitting problems, the selection of knots in order to get an optimized curve for a shape design is well-known. For large data, this problem needs to be dealt with optimization algorithms avoiding possible local optima and at the same time getting to the desired solution in an iterative fashion. Many evolutionary optimization techniques like genetic algorithm, simulated annealing have al...
متن کاملEngineering Evolutionary Algorithm to Solve Multi-objective OSPF Weight Setting Problem
Setting weights for Open Shortest Path First (OSPF) routing protocol is an NP-hard problem. Optimizing these weights leads to less congestion in the network while utilizing link capacities efficiently. In this paper, Simulated Evolution (SimE), a non-deterministic iterative heuristic, is engineered to solve this problem. A cost function that depends on the utilization and the extra load caused ...
متن کاملAccelerating Multiobjective Vlsi Cell Placement with Parallel Evolutionary/tabu Search Heuristics
Multiobjective combinatorial optimization problems in various disciplines remain to be the focus of extensive research due to their inherent hard nature and difficulty of finding near-optimal solutions. Iterative heuristics like Tabu Search (TS) and Simulated Evolution (SimE) have successfully been employed to solve a range of such optimization problems [1]. These heuristics are able to obtain ...
متن کامل