Generic Simulated Evolution and Stochastic Evolution

The problem of Multiple Sequence Alignment is regarded as one of the many issues that are categorized as NP-Hard. Firstly, it necessitates, like any other such system, a specific encoding scheme and precise design of the key characteristics of the heuristic algorithm being created and implemented. This paper aims to clarify our suggested universal interpretation of the stochastic evolution (Stoc-E) and Simulated Evolution (SimE) algorithms. Undoubtedly, heuristic search methods such as Sim-E and Stoc-E are more effective than the simplified local search methods observed in Genetic Algorithms (GA), Simulated Annealing (SA), and Tabu Search (TS), which we have already analyzed, for tackling these kinds of problems. Although other works in the literature have examined the use of refined methodology or a restricted set of heuristics, we have steadfastly adhered to our original objective of aligning as many heuristics as feasible, even if these two algorithms are better suited for the placement category of algorithms. Hence, we first developed a thorough encoding scheme that is compatible with all the above techniques. Consistent with our prior study, we are simultaneously presenting several pseudo-code options for these algorithms, together with the capability to trace functions, which will provide further examination for the purpose of adjusting starting parameters.