Discussing the article: "Population-Based Optimization Algorithms: Building a Defense Against Cheaters"

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Check out the new article: Population-Based Optimization Algorithms: Building a Defense Against Cheaters.

The algorithms were rerun on the updated functions, and a method was proposed for quickly verifying their "honesty". The composite test combines five different landscapes and eliminates the possibility of gaining an advantage based on the geometry of individual problems, allowing for a quick assessment of the algorithm's actual search capability. A script is provided for preliminary validation of algorithms before they are applied to the optimization of trading strategies.

In the previous article, we found that certain algorithms can receive inflated scores not because of the quality of their search, but because of a chance match between their internal mechanics and the geometry of the test functions. We identified the nature of the phenomenon, formulated diagnostic criteria, and adjusted the Forest and Megacity test functions. This eliminated the possibility of gaining an advantage due to search agents moving "diagonally" in a two-dimensional plane. The article concluded with a plan: to run all the algorithms on the updated test bench and recalculate the rankings.

This article carries out that plan. All 45 algorithms from the ranking table were retested using the adjusted test functions. In addition to the recalculation, a composite test for detecting “cheating” was developed. Instead of optimizing a single function with repeating coordinates, the agent simultaneously optimizes five different functions with different landscapes. An algorithm with an artificial directional bias cannot simultaneously “guess” the optimum on five different surfaces, whereas an honest search mechanism will show very similar results on both the updated test functions and the new multi-function test. Its purpose is to quickly and definitively determine whether an algorithm has actual search capability before running the full test bench. This is a kind of entry filter that weeds out dummy algorithms at an early stage. This is not about revising the methodology, but about eliminating a few exceptions that exploited the specific location of the extrema.

Author: Andrey Dik