implementation of Metaheuristics(Genetic Algorithms) in Python
Budget: €30 – €50 EUR
Step 1) Select and implement a solution representation suitable for your metaheuristic, probably
adapt the heuristic method such that it provides you solutions in a way you can use it.
Step 2) Define, implement & test local search methods (in the case of local search-based metaheuristics) or define, implement & test ways of generating new solutions (population-based metaheuristics, e.g. cross-over for GA)
Step 3) Implement and test the other components of your metaheuristic. The goal is to have a single function used to perform the metaheuristic. The various parameters should be given as arguments to that function (e.g., def tabu search (starting_solution, tabulist_length, stopping_criteria) )
Step 4) Testing and perform experiments to identify final design decisions (probably different options of local search, mutation operations etc) and parameter settings (balancing diversification and intensification, stopping criteria)
Step 5) Perform final testing
adapt the heuristic method such that it provides you solutions in a way you can use it.
Step 2) Define, implement & test local search methods (in the case of local search-based metaheuristics) or define, implement & test ways of generating new solutions (population-based metaheuristics, e.g. cross-over for GA)
Step 3) Implement and test the other components of your metaheuristic. The goal is to have a single function used to perform the metaheuristic. The various parameters should be given as arguments to that function (e.g., def tabu search (starting_solution, tabulist_length, stopping_criteria) )
Step 4) Testing and perform experiments to identify final design decisions (probably different options of local search, mutation operations etc) and parameter settings (balancing diversification and intensification, stopping criteria)
Step 5) Perform final testing
Related categories:
Python
Algorithm
Software Architecture
Machine Learning (ML)
Combinatorial Optimization