Backtracking Search Algorithm (BSA)

BSA is an evolutionary population-based optimizer proposed by Pinar Civicioglu (2013) for real-valued numerical optimization. Its signature is memory: it keeps a historical (archive) population alongside the current one and moves "with an eye on past experience." Source: andrey-dik's MQL5 implementation (C_AO_BSA_Backtracking); minimal params (population size, mixrate).

How it works (four steps per iteration)

1. Selection-I — with ~50% probability the historical population is refreshed from the current one, then randomly shuffled for diversity.
2. Mutation — M = P + F·(oldP − P), with F a normally-distributed amplitude; movement relative to historical positions.
3. Crossover — either "mixrate" (take several coordinates from the current pop) or a single random coordinate.
4. Selection-II — greedy: keep a trial only if it improves fitness.

Benchmark (context-relative)

Ranked 20/45 (55.10%) on the population-optimization-benchmark — the best of the founding four — "a compromise between ease of implementation and search efficiency," a stable mid-pack performer. (Benchmark-relative; see no-free-lunch-theorem.)

Why T2 (re-tiered 2026-08-25)

Held at T3 since ingest, which graded the venue — MQL5, a broker's community article site — rather than what the page rests on. This page's load-bearing claims are a mechanism description of an algorithm published by Civicioglu (2013) and a comparative score, both from andrey-dik, who did not write the algorithm and has no stake in where it lands. That is somebody measuring somebody else's work, which QUALITY.md's ladder puts at T2.

The spoke's standing finding — that in this field the scorer is usually the author — is real and does not apply here; it applies to DOAm, which stays T3 because there Dik is scoring his own modification. The confound that does remain is implementation: Dik re-implements each optimizer in MQL5, so his figure grades his code as much as the method, and his scoring scheme changed mid-series (population-optimization-benchmark). Both stay flagged in prose, where a methodological limit belongs — not in the tier.

Note the floor is unmoved by this: T1 ÷ tiered counts T1 against the same denominator either way, so the change alters the tier mix and not the spoke's floor of 0.18.

Related

metaheuristic-optimization · exploration-vs-exploitation · population-optimization-benchmark · no-free-lunch-theorem · andrey-dik · cma-es