Epigenetics Algorithms: Self-Reinforcement-Attention mechanism to regulate chromosomes expression
This work addresses optimization challenges in evolutionary computation by introducing an epigenetics-inspired method, though it appears incremental as it builds on existing genetic algorithms with new operators.
The paper tackles the problem of improving genetic algorithms by modeling epigenetics operators, specifically DNA methylation, and demonstrates that the proposed epigenetics algorithm efficiently solves complex non-convex optimization problems, showing good performance and an ability to find global optima.
Genetic algorithms are a well-known example of bio-inspired heuristic methods. They mimic natural selection by modeling several operators such as mutation, crossover, and selection. Recent discoveries about Epigenetics regulation processes that occur "on top of" or "in addition to" the genetic basis for inheritance involve changes that affect and improve gene expression. They raise the question of improving genetic algorithms (GAs) by modeling epigenetics operators. This paper proposes a new epigenetics algorithm that mimics the epigenetics phenomenon known as DNA methylation. The novelty of our epigenetics algorithms lies primarily in taking advantage of attention mechanisms and deep learning, which fits well with the genes enhancing/silencing concept. The paper develops theoretical arguments and presents empirical studies to exhibit the capability of the proposed epigenetics algorithms to solve more complex problems efficiently than has been possible with simple GAs; for example, facing two Non-convex (multi-peaks) optimization problems as presented in this paper, the proposed epigenetics algorithm provides good performances and shows an excellent ability to overcome the lack of local optimum and thus find the global optimum.