Selecting for Selection: Learning To Balance Adaptive and Diversifying Pressures in Evolutionary Search
This work addresses a key challenge in evolutionary algorithms for researchers and practitioners dealing with deceptive fitness landscapes, though it is incremental as it builds on existing selection methods.
The paper tackled the problem of balancing exploration and exploitation in evolutionary search by introducing Sel4Sel, a meta-evolutionary algorithm that learns neural-network-based selection functions, resulting in performance that matched or exceeded benchmarks on three bitstring domains.
Inspired by natural evolution, evolutionary search algorithms have proven remarkably capable due to their dual abilities to radiantly explore through diverse populations and to converge to adaptive pressures. A large part of this behavior comes from the selection function of an evolutionary algorithm, which is a metric for deciding which individuals survive to the next generation. In deceptive or hard-to-search fitness landscapes, greedy selection often fails, thus it is critical that selection functions strike the correct balance between gradient-exploiting adaptation and exploratory diversification. This paper introduces Sel4Sel, or Selecting for Selection, an algorithm that searches for high-performing neural-network-based selection functions through a meta-evolutionary loop. Results on three distinct bitstring domains indicate that Sel4Sel networks consistently match or exceed the performance of both fitness-based selection and benchmarks explicitly designed to encourage diversity. Analysis of the strongest Sel4Sel networks reveals a general tendency to favor highly novel individuals early on, with a gradual shift towards fitness-based selection as deceptive local optima are bypassed.