Crossing genetic and swarm intelligence algorithms to generate logic circuits

Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. On the other hand, Particle swarm optimization (PSO) is a population based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as GAs. The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. PSO is attractive because there are few parameters to adjust. This paper presents hybridization between a GA algorithm and a PSO algorithm (crossing the two algorithms). The resulting algorithm is applied to the synthesis of combinational logic circuits. With this combination is possible to take advantage of the best features of each particular algorithm

Combining genetic and particle swarm algorithms for the design of combinational circuits

Evolutionary computation (EC) is a growing research field of Artificial Intelligence (AI) and is divided in two main areas: the Evolutionary Algorithms (EA) and the Swarm Intelligence (SI). This paper presents an algorithm that combines an EA algorithm - the Genetic Algorithm (GA) with a SI algorithm - the Particle Swarm Optimization Algorithm (PSO). The new algorithm is applied to the synthesis of combinational logic circuits. With this combination is possible to take advantage of the best features of each particular algorithm.N/

An evolutionary approach to the synthesis of combinational circuits

This paper proposes a genetic algorithm for designing combinational logic circuits and studies four different case examples: 2-to-1 multiplexer, one-bit full adder, four-bit parity checker and two-bit multiplier. The objective of this work is to generate a functional circuit with the minimum number of gates.N/

New Concepts Towards the Synthesis of Digital Circuits Through Genetic Algorithms

This paper analyses the performance of a Genetic Algorithm using two new concepts, namely a static fitness function including a discontinuity measure and a fractional-order dynamic fitness function, for the synthesis of combinational logic circuits. In both cases, experiments reveal superior results in terms of speed and convergence to achieve a solution

A Memetic Algorithm for Logic Circuit Design

Memetic Algorithms (MAs) have shown to be very effective in solving many hard combinatorial optimization problems. In this perspective, this paper presents a MA for combinational logic circuits synthesis. The proposed MA combines a Genetic Algorithm (GA) for digital circuit design with the gate type local search (GTLS). The combination of a global and a local search is a strategy used by many successful hybrid optimization approaches. The main idea is to apply a local refinement to an Evolutionary Algorithm (EA) in order to improve the fitness of the individuals in the population. The obtained results indicate that the MA reduces the number of generations required to reach the solutions and its standard deviation while improves the final fitness function.N/

Population size and processing time in a genetic algorithm

This paper presents several experiments with a genetic algorithm (GA) for designing combinational logic circuits. The study addresses the population size and the processing time for achieving a solution in order to establish a compromise between the two parametrs. Furthermore, it is also investigated the use of different gate sets for designing the circuits namely RISC and CISC like gate sets.info:eu-repo/semantics/publishedVersio

Evolutionary design of combinational circuits using fractional-order fitness functions

This paper analyses the performance of a genetic algorithm using the new concept of fractional-order dynamic fitness function, for the synthesis of combinational logic circuits. The experiments reveal superior results in terms of speed and convergence to achieve a solution.N/

Digital Circuit Design Using Dynamic Fitness Functions

This paper proposes and analyses the performance of a Genetic Algorithm using two new concepts, namely a static fitness function including a discontinuity measure and a fractional-order dynamic fitness function, for the synthesis of combinational logic circuits. In both cases, experiments reveal superior results in terms of speed and convergence to achieve a solution.N/

Fractional Dynamic Fitness Functions for GA-based Circuit Design

This paper proposes and analyses the performance of a Genetic Algorithm (GA) using two new concepts, namely a static fitness function including a discontinuity measure and a fractional-order dynamic fitness function. The GA is adopted for the synthesis of combinational logic circuits. In both cases, experiments reveal superior results in terms of speed and convergence to achieve a solution.N/