In biology, a genome refers to the complete set of genetic material or DNA present in a species. This genetic material contains all the instructions necessary for the development, functioning, and reproduction of the species. Over time, genome evolution happens to enable an organism to adapt to changing environments and to optimize their survival and reproduction.
One of the fundamental drivers of genome evolution is mutation. Mutations can lead to the development of new traits, changes in gene function, and ultimately, the diversification and adaptation of species over time. Diversity and adaptability are two key aspects determining how good or bad the process of mutation in genome evolution is.
Genetic Diversity and Adaptability
High genetic diversity provides variability necessary for species to respond to changing environments. If genomes carry genes which are different to other genomes it is most likely to create more different new or modified genomes (mutated genomes) during the mutation process. Thus, increasing the rate of a mutated genome to survive in the changing environment.
High genetic adaptability preserves advantageous genes. When a mutated genome provides a survival or reproductive advantage, species carrying that mutated genome are more likely to pass it on to the next generation through natural selection. Over time, advantageous mutations can become more common in a population, leading to adaptation to the environment.
In other words, genome evolution is the optimization process of a genome to improve survival and reproduction, and this concept is inspiring the invention of evolutionary optimization algorithms seen in the world of mathematics, such as Genetic Algorithm (GA), Differential Evolution (DE), Evolutionary Strategies (ES) and etc. The creation of these algorithms is extensively applied in modern optimization problems, ranging from financial portfolio management to complex engineering design.
Inspiring modern evolutionary optimization algorithm
In the realm of engineering design, engineers frequently find themselves tasked with enhancing their design structures to yield optimal performance, aligning with specified design criteria. For RF engineers, it’s customary to fine-tune and optimize designs during the initial phases before proceeding to the fabrication of RF filters. Throughout this optimization process, the aforementioned optimization algorithm comes into play, driving the refinement of design parameters toward an optimal configuration that ensures a satisfactory design response. In this context, the term “genome” refers to the collective assembly of design parameters, with each individual gene within the genome corresponding to a distinct parameter within the set.
To apply genome evolution in the optimization, first the mentioned “changing environment” is needed for genomes to evolve. The environment can be pictured using the response surface of a optimization problem:
In Figure 4, a response surface is shown with several purple bordered white dots scattered around. The dot is imagined as a genome or a set of design parameters in the engineering context. The dots in the more red area are the weak genomes / imperfect set of design parameters. The dots in the more blue area are the strong genomes / close to perfect set of design parameters.
Although initially there are multiple sets of design parameters that are not giving good design response, but over time in the optimization process, “mutations” across sets of design parameters will continuously pass down the “advantageous” parameter’s values from one to another, eventually driving the “mutated” design parameters closer to the optimal state (blue area in the response curve).
Application of Genome Evolution Concept into Optimization
To enhance the effectiveness of such genetic evolutionary processes, two key aspects must be fulfilled, diversity of the design parameters and how each of the good parameters pass down to the next solution in the mutation process. There exist many sampling techniques such as Latin-Hypercube, Sobol, Halton and etc to help to improve diversity of the design parameters set, and they are used in the initial stage before the optimization process. This is to ensure that each set of design parameters (solution) is different to each other, providing more choices of design parameters in the mutation process.
To keep things simple, the session below demonstrates how a simple differential evolution algorithm utilizes the idea of genome evolution to perform optimization. The mutation process of one set of design parameters is illustrated below:
Step 1: Selection
In step 1, a set of design parameters is focused (target). There are 3 candidates chosen randomly. The number of candidates chosen has no fixed rule of thumbs, it depends on the requirement and design complexity.
Step 2: Mutation
The 3 candidates undergo a mutation process and a mutate is obtained. This mutate is a modified version of the set of the design parameters from mutation of the candidates.
Step 3: Crossover
The target and the mutate will undergo a crossover process to output a modified set of the design parameters, classified as trial.
Step 4: Evaluation
The target is replaced by trial if trial has higher fitness (lower cost), otherwise the target remains unchanged. Then the process is iterating for the rest of the genome / set of design parameters.
Summary
To summarize, genome evolution, in the context of optimization, is a dynamic process inspired by the principles of biological evolution. It involves the continuous refinement and adaptation of genetic information (the genome) to improve the performance of solutions in response to specific optimization goals. To ensure effectiveness of the process, the genetic diversity and the mechanism to ensure strong genetic information must be preserved. Through mechanisms like mutation, recombination, and selection, genomes evolve over generations to achieve better fitness or performance in solving complex problems across various fields, including engineering, biology, and artificial intelligence. This iterative process of optimization-driven genome evolution plays a crucial role in finding efficient solutions to intricate real-world challenges.
Next Exploration
Mutation process is a good process to drive solution evolution towards optimality. However, there are limitations where many times the mutation process might lead to prematurely converged solutions, if the diversity of the sets of solutions is not robust enough or the advantageous design parameters are lost in the mutation process. Thus, requiring further enhancement to the fundamental aspect of preserving good genes in the genome. Our next exploration will be in the following to study how a good gene is preserved, or even how a prematurely converged solution can have a higher chance to jump out of the trap.
- Genetic Elitism — Elitism ensures the promising areas of the search space — where local or global optima are found — can be continuously exploited across generations. It enforces the following conditions:
The straightforward inclusion of the best individuals of a generation in the population of the following generation
The use of the best individuals of a generation a minimum number of times to crossover with other random individuals for creating the following generations
- Pareto Analysis — Study how vital few features of the genome can actually have more contributions to the goodness of a genome.
Reference
Originally published at http://filpal.wordpress.com on September 3, 2023.
