To get started with PyGAD , simply import it. Using PyGAD , a wide range of problems can be optimized. A quick and simple problem to be optimized using the PyGAD is finding the best set of weights that satisfy the following function:. A very important step is to implement the fitness function that will be used for calculating the fitness value for each solution. Here is one. Next is to prepare the parameters of PyGAD.
Here is an example for a set of parameters. After the parameters are prepared, an instance of the pygad. GA class is created. After creating the instance, the run method is called to start the optimization. After the run method completes, information about the best solution found by PyGAD can be accessed. Problems with high dimensionality or very large variable spaces become impossible for deterministic methods to solve in a reasonable amount of time. Stochastic algorithms can search the variable space and dynamically bias the candidate generation toward the optimum solution.
Stochastic algorithms are also crucial in situations where deterministic methods cannot be applied, for example when a machine part will fail, optimum critical path problems, decay fission products, etc. GAs utilize stochastic operations such as crossover and mutation on a population to make a change of generation. Crossover combines substructures of parents to produce new individuals.
Crossover is the core of the genetic algorithm and is what sets it apart from other stochastic methods, such as simulated annealing [25]. Mutation is another operation that helps the algorithm to prevent local convergence and search the global variable space. A simple genetic algorithm with operators such as crossover, mutation, and elitism yields goods result in practical optimization problems compared to a deterministic algorithm [2,5].
Not much work has been done on understanding the effect of the genetic algorithm operators such as crossover, mutation, and elitism on the function convergence. Stochastic algorithms have some inherent drawbacks when compared to deterministic methods. First and foremost is the fact that the exact optimum is never actually achieved [26].
The optimum candidate can be very close to the actual optimum, however, there is no way of knowing for certain that the optimum candidate is at the global maximum or minimum. Another potential problem is the rate of convergence.
For some algorithms, such as simulated annealing, tuning parameters allow for rapid or reduced convergence [27]. If the algorithm is forced to converge too quickly, the chances of attaining the optimum become very low as the system converges on a chaotic state. One of the biggest disadvantages of using a genetic algorithm is that the GA cannot assure constant optimization response times. This unfortunate genetic algorithm property limits the use of genetic algorithms in real-time applications.
Certain optimization problems called variant problems cannot be solved by means of genetic algorithms. This is mainly due to poorly known fitness functions that generate bad chromosome blocks in spite of the fact that only good chromosome blocks cross over. On the other hand, the hybridization of the genetic algorithm with a deterministic algorithm helps to overcome these problems and to achieve the globally optimum solution applicable to any industry that requires optimization, especially for problems with high dimensionality and large domain spaces that are not readily solved by traditional deterministic or stochastic algorithms.
This study has proved the hypothesis that hybridization of the genetic algorithm with a deterministic algorithm improves the optimum solution obtained by statistical methods alone.
Our current research work is on generating a Pareto front using the hybrid genetic algorithm. By introducing an objective function, a series of viable solutions can be determined that can optimize the yield of the process.
The Pareto front generated for an objective function can help the process managers make decisions on what optimal conditions they are interested in running in order to maximize the yield or minimize the cost. Conclusions The present research was on understanding the effect of hybridization of a genetic algorithm with a gradient-based method on function optimization. The hybrid genetic algorithm developed was tested on optimizing the Ackley benchmark optimization function, the anthocyanin yield, fatty acid methyl ester FAME yield, and xylanase activity functions published in the literature.
The minimum value obtained using the hybrid genetic algorithm for the Ackley function was 9. In food processing, the maximum anthocyanin yield obtained was The maximum anthocyanin yield was achieved at a lower liquid-to-solid ratio and ethanol concentrations. In biodiesel production, the hybrid genetic algorithm predicted a maximum of The hybrid genetic algorithm optimum indicated that these higher yields are achievable at reduced catalyst and reaction time.
In enzyme production, the HGA predicted a maximum yield of The results show that the hybrid genetic algorithm predicted better optimized process conditions and product yields compared to regular statistical methods.
Accordingly, the publisher, by accepting the article for publication, acknowledges that the U. Author Contributions: Jaya Shankar Tumuluru and Richard McCulloch conceived the ideas, developed and tested the hybrid genetic algorithm, and wrote the paper. Conflicts of Interest: The authors declare there is no conflict of interest. Tumuluru, J. Changes in moisture protein and fat content of fish and rice flour coextrudates.
Food Bioprocess Technol. Shankar, T. Optimization of extrusion process variables using genetic algorithm. Food Bioprod. Process variables during single screw extrusion cooking of fish and rice flour blends. Food Process. Khan, M. Foods , 5, 76 13 of 13 5. A case study on optimization of biomass flow during single-screw extrusion cooking using genetic algorithm GA and response surface method RSM.
Effect of process variables on the density and durability of pellets made from high moisture corn stover. Pelletization of high moisture corn stover using a flat die pellet mill with 6 mm die: Physical properties and specific energy consumption.
Energy Sci. Specific energy consumption and quality of wood pellets made from high moisture lodgepole pine biomass. Khuri, A. Ravindra, M. Optimisation of osmotic preconcentration and fluidised bed drying to produce dehydrated quick-cooking potato cubes. Food Eng. Madamba, P.
Optimization of the osmotic dehydration of mango Mangifera Indica L. Azoubel, P. Mass transfer kinetics of osmotic dehydration of cherry tomato. Liu, X. Optimisation of aqueous two-phase extraction of anthocyanins from purple sweet potatoes by response surface methodology. Food Chem. Lee, H. Process optimization design for jatropha-based biodiesel production using response surface methodology. Fuel Process Technol. Vimalashanmugam, K. Holland, J. Genetic Algorithms. Deb, K. April, J. Practical Introduction to Simulation Optimization.
It was found that, among 8, 12, firmed by genetic algorithms. However, it should be said that and 16 bits, the 12 bit option gives the best results in reservoir ground levels, not specified in the paper by Jowitt generations. In fact, if the bit number is too small, the algo- and Xu , were chosen equal to 40 m, and this could rithm converges rapidly, but the precision is not sufficient to make some difference in the results.
The genetic algorithm allows one to obtain results when the nonlinear optimization algo- rithm does not converge. In particular, with the nonlinear op- timization algorithm, it is not possible to obtain complete re- sults with valves , , and , because of oscillating solutions in a few two hour intervals.
Subsequently, the floating point algorithm was applied and compared with the binary algorithm. The comparison shows FIG.
Discussion by L. Tong,3 and G. Michalewicz, Z. However, the discussers feel that a few Pezzinga, G. Costruzioni Idrauliche, Vol. Porto, watershed i. These variables are directly related to the phos- and F. Chaudhry8 phorus level in the reservoir. However, many other human ac- tivities may affect the phosphorus level, including the cropping area, livestock husbandry, soil conservation practices, land use The discussers have presented interesting comments on our plans, and forest coverage.
Consequently, a good decision for paper. They report on their work on optimal location of valves phosphorus-fertilizer control may not be good for the entire employing different optimization techniques. The writers also watershed system, since many other activities were not con- observed, by comparison between the successive optimal valve sidered in the systems analysis.
For example, to reduce phos- location combinations, that the hypothesis adopted by the dis- phorus concentration in the reservoir, one can either reduce cussers held during our complete simulations. The writers con- the fertilizer application per unit cropping area or reduce the sider the assumption that each optimal combination of NV total cropping area. From this point are related to the water quality objective of the reservoir.
Crops of view, their decision to use genetic algorithms for the deter- need water and generate non-point source pollutants from fer- mination of valve openings is quite justified.
However, there tilizer application. Livestock husbandry brings economic ben- is a price to pay in terms of computational time when a search efits, but it also discharges wastewater to the reservoir. For method replaces a mathematical optimization program — in such a complicated system, more constraints for defining re- this case, the linear programming method.
However, it should be noted from Tables 1 and 6 that these two combinations compete very closely with each 1. Phosphorus loss constraints: other and have nearly the same reduction of water losses. Learn About Live Editor. Select a Web Site. Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select:. Select the China site in Chinese or English for best site performance.
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