This paper introduces a novel hybrid evolutionary algorithm to solve the 2019 IEEE CEC Competition on 100-Digit Challenge on Single Objective Numerical Optimization. The proposed algorithm, named GADE, employs a genetic algorithm (GA) to explore the search space. If a complete solution is not found with GA, differential evolution (DE) exploits the search space using the latest GA solution candidates. We present two techniques to improve GA's exploration capabilities: fitness-based opposition and tidal mutation. Simulations on the ten challenge problems indicate that fitness-based opposition allows more GA simulations to find the correct digits. Also while GA and DE can fully solve four of the problems on their own, the hybrid algorithm allows for higher scores in at least three of the remaining problems. Furthermore, we provide analysis on the contribution of each EA to the score based on their cost function evaluations.
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