# Extended Evolutionary Algorithms with Stagnation-Based Extinction Protocol
## 1 Introduction  
The biological metaphor of evolution was being applied to computation since as early as 1950 [1; 2; 3], and the genetic algorithm (GA) is one of the evolutionary algorithms inspired by this process of natural evolution. The theory of evolution was introduced by [PERSON] in 1858 [4] and together with [PERSON]'s theory of natural selection [5] and [PERSON]'s concept of genetics [6] they formed the neo-Darwinian paradigm [7] which the genetic algorithm was based on. Although it is clear that in the process of evolution, there are fundamental components such as natural selection, reproduction (crossover), and mutation, it is often neglected that extinction plays an important role in this process too. Several evolutionary biologists have raised this question and argued that extinction plays a significant role in evolution [4; 8], but little has been done in the field of evolutionary computation where extinction is still being neglected or ignored.  
When we take a look at the efforts in improving the performance of GA, we can categorize the efforts into two groups: One tries to improve GA through modeling GA closer to natural evolution by introducing new biological operators or modifying existing ones to closely mimic processes observable in nature [9; 10; 11]. The other group mainly designs operators that were tailored to suit specific problems, and these operators have no correspondence to nature of any sort whatsoever [12; 13; 14]. Based on the increased performance and accuracy of GAs that were modeled to closely mimic the natural evolution process, we came to a deduction: the closer we model our GA towards natural evolution, the better it will perform. With this in mind, this research aims to introduce a novel genetic algorithm inspired by the extinction phenomenon in nature: stagnation-driven extinction protocol for genetic algorithm (SDEP-GA).  
The idea of SDEP-GA is analogous to Dropout regularization [15]. In training the neural network, Dropout operation is randomly omitting a subset of hidden units at each training iteration in the neural network. This random removal of hidden units at each training iteration turns out to be a combination of exponentially many different neural networks which share the same weights. Thus, Dropout regularization basically attempts to average multiple models to improve the performance of the neural network. [PERSON] et al. [16]have shown that dropout reduces overfitting and improves generalization. Removing chromosomes in SFEP-GA is similar to omitting hidden units in dropout, but in SDEP-GA, removing chromosomes with the extinction probability in a random way or a targeted way can allow greater possibility of exploration for new solutions.  
The remaining of this paper is organized as follows. Section 2 presents a description of GA, specially focusing on simple GA. Section 3 provides the design and architecture of our proposed algorithm, stagnation-driven extinction protocol for GA (SDEP-GA). Section 4 will cover the experimental design of our proposed algorithm together with the experimental results and discussion of SDEP-GA compared against Simple GA in terms of performance. Finally, Section 5 gives a summary, a review of our contribution, and possible directions for future work.