In this video, I’m going to show you a Python code of my adaptive re-start hybrid genetic algorithm for global optimization. Genetic Algorithm (GA) is one of the most general global optimisation solution methods used in many research fields. However, like other search techniques, GA has weak theoretical guarantee of global optimal solution and can only offer a probabilistic guarantee.
Having a GA capable of searching for the global optimal solution with very high success probability is always desirable. In this video, I will show you an innovative structure of GA, in which adaptive restart and local search mechanism are harmoniously integrated together, to improve the success rate of achieving global optimal solution of the algorithm. Robustness of this GA is demonstrated through a famous, difficult global optimization problem.
Let's see.
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Python code: https://bit.ly/3lSIwEo
HERE ARE 6 LISTS OF MY VIDEOS YOU MAY BE INTERESTED IN:
1. Optimization Using Genetic Algorithm:
• Optimization Using Genetic Algorithms
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3. Optimization Using Simulated Annealing Algorithm:
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4. Optimization Using Optimization Solvers:
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5. Optimization Using Matlab:
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6. Optimization Using Python:
• Optimization Using Python
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