(Holland, 1975).Fig. 9illustrates the flowchart of the standard processes involved when performing genetic algorithm. The algorithm works by initialising a competitive set of possible solution candidates, e.g.
Mutation operation maintains the stochastic nature of the algorithm by maintaining the genetic diversity of current generation to the next one [22,25]. Mutation and crossover operation with an example is demonstrated in Fig. 5. The general flowchart of GA optimization process is shown in Fig. 6...
This paper proposed a walking control algorithm for the soft quadruped robot by using genetic algorithms to optimize PID parameters. The research has successfully applied the conventional PID controller to the soft robot. Additionally, the proposed method can be implemented for the actual control of ...
The conceptual model is developed and modeled, and solutions are explored using General Algebraic Modeling System software (GAMS), as well as Multiple Objective Particle Swarm Optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGAII) algorithms in small dimensions (Freitas et al.,...
possibilities in the brownfield case. With a scalability improvement through computation time decrease of up to∼2.75×, reduced number of equipment and workstations, but worse objective values, the genetic algorithm holds the potential for reconfiguring assembly lines. However, the genetic algorithm...
Flowchart of genetic algorithm. 5. Example We conducted computational experiments to evaluate the proposed algorithm. The algorithm was coded in MATLAB and run on a PC (Intel Pentium 4, 2.8 GHz, 2 GB memory). There is a batch of bananas from southern China that will be distributed to ...
Flowchart for Adaptive Genetic Algorithm (AGA). Full size image Results and Discussion In this section we present four novel applications that are successfully solved with the AGA technique. Each of these problems is chosen carefully to represent the complexity of binary-pattern metasurface design and...
a multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm (INSGA-II) is proposed. Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves. Then, an INSGA-II, by introdu...
Fig. 2. The proposed algorithm flowchart. In the LGA, each chromosome consists of two parts: gene (in abbreviation, G-part) and learning automaton (in abbreviation, LA-part) (see Fig. 3(a)). The G-part, similar to the standard GA, is used in the evolution phase, and the LA-part...
Evolutionary algorithms seem particularly suitable to solving multiobjective optimization problems because they deal simultaneously with a set of possible solutions (or a population). This allows designers to find several members of the Pareto optimal set in a single run of the algorithm, instead of ...