Fast Immune System-Inspired Hypermutation Operators for Combinatorial Optimization

Various studies have shown that immune system-inspired hypermutation operators can allow artificial immune systems (AIS) to be very efficient at escaping local optima of multimodal optimization problems. However, this efficiency comes at the expense of considerably slower runtimes during the exploitation phase compared to the standard evolutionary algorithms. We propose modifications to the traditional hypermutations with mutation potential (HMP) that allow them to be efficient at exploitation, as well as maintaining their effective explorative characteristics. Rather than deterministically evaluating fitness after each bit-flip of a hypermutation, we sample the fitness function stochastically with a parabolic distribution. This allows the stop at the first constructive mutation (FCM) variant of HMP to reduce the linear amount of wasted function evaluations when no improvement is found to a constant. The stochastic distribution also allows the removal of the FCM mechanism altogether as originally desired in the design of the HMP operators. We rigorously prove the effectiveness of the proposed operators for all the benchmark functions, where the performance of HMP is rigorously understood in the literature. We validate the gained insights to show linear speed-ups for the identification of high-quality approximate solutions to classical NP-Hard problems from combinatorial optimization. We then show the superiority of the HMP operators to the traditional ones in an analysis of the complete standard Opt-IA AIS, where the stochastic evaluation scheme allows HMP and aging operators to work in harmony. Through a comparative performance study of other fast mutation operators from the literature, we conclude that a power-law distribution for the parabolic evaluation scheme is the best compromise in black-box scenarios, where little problem knowledge is available.

Automated summary

This study proposes modifications to traditional hypermutation operators to improve efficiency during the exploitation phase while maintaining effective explorative characteristics. By using a stochastic parabolic distribution for fitness function sampling, wasted function evaluations are reduced. The proposed operators are rigorously proven to be effective for benchmark functions and show linear speed-ups in identifying high-quality approximate solutions to NP-Hard problems. Through comparative performance studies, it is concluded that a power-law distribution for the parabolic evaluation scheme is the best compromise in scenarios with limited problem knowledge.