I have a genetic algorithm for an optimization problem. I plotted the running ti

Question

I have a genetic algorithm for an optimization problem. I plotted the running time of the algorithm on several runs on the same input and the same parameters (population size, generation size, crossover, mutation).

The execution time changes between executions. Is this normal?

I also noticed that against my expectation the running time sometimes decreases in place of increasing when I run it on a larger input. Is this expected?

How can I analyze the performance of my genetic algorithm experimentally?

Explanation / Answer

For example, See the figure 3 of this paper: A Building-Block Royal Road Where Crossover is Provably Essential where performance of various GA are compared against each other.

The plot shows changes in fitness (Y-axis) vs iteration number (X-axis). Each algorithm is run multiple times and the average, min and max fitness is shown in the plot. Hence, showing clearly some GA variation have better performance than others.

The asymptotic convergence of fitness over iteration as suggested by vzn's answer is also very useful for most cases.

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