Bio-inspired Algorithm Configuration Sheet
Define parameters, pseudocode logic, and environmental factors for optimization algorithms like GA, PSO, and ACO.
BIO-ALGORITHM CONFIGURATION SHEET
Algorithm: | Focus:
Algorithm: Genetic Algorithm (GA)
Optimization Focus: Minimizing Logistics Cost
1. Algorithm Definition & Objective
A robust, population-based metaheuristic optimization algorithm inspired by natural selection. It is used to find optimal solutions by iteratively applying selection, crossover, and mutation operators to a set of candidate solutions (chromosomes).
Problem Space
Continuous (Real-valued)
Stopping Criteria
100 Generations
Fitness Function
1 / Cost(x)
2. Pseudocode Core Logic
INITIALIZE Population (P)
WHILE (Stopping criteria not met):
EVALUATE Fitness of P
SELECT Parents (P')
GENERATE Offspring (P'') by Crossover
MUTATE P''
P = P' + P'' (New Generation)
END WHILE
RETURN best solution
3. Configured Parameters
| Parameter | Value | Rationale / Notes |
|---|---|---|
| Population Size (N) | 50 | Larger population slows iteration but increases diversity. |
| Crossover Rate (Pc) | 0.8 | High probability encourages exploration. |
Algorithm Identity
Parameter Values
Define the control parameters for the algorithm. These are highly specific to the selected algorithm type.
Format: **Parameter Name | Value | Rationale / Notes**
Example: *Inertia Weight (w) | 0.7 | Balances global vs. local search (PSO).*
Example: *Inertia Weight (w) | 0.7 | Balances global vs. local search (PSO).*