Day 20: Constraint Handling in Fitness Functions: Penalizing Bad Solutions
Genetic algorithms are powerful optimization tools, but real-world problems often involve constraints that cannot be ignored. In scheduling, routing, resource allocation, and layout optimization, constraints like resource limits, timing conflicts, and exclusivity rules define what makes a solution valid. Without a mechanism to enforce or guide adherence to these rules, a genetic algorithm may evolve highly fit but completely infeasible solutions.
One effective way to handle this is by integrating constraint penalties directly into the fitness function. By penalizing violations, we make sure that invalid solutions are less likely to survive and propagate in future generations. This method requires designing fitness functions that are both accurate evaluators and adaptive enforcers of your problem’s rules.