Obliczenia ewolucyjne i inteligencja roju

Evolutionary computation and swarm intelligence · id evolutionary · aliases: evolutionary computation, evolutionary algorithms, nature-inspired optimization, metaheuristics, algorytmy ewolucyjne, obliczenia ewolucyjne, metaheurystyki

Populacyjne metody optymalizacji i przeszukiwania inspirowane ewolucją i zachowaniami zbiorowymi, działające bez gradientu, tylko na ocenach kandydatów.

Population-based optimisation and search methods inspired by evolution and collective behaviour, working without gradients, only on candidate scores.

In one sentence

Rodzina metod, które szukają dobrych rozwiązań jak natura: tworzą wiele wariantów, sprawdzają je i rozwijają najlepsze.

A family of methods that search for good solutions the way nature does: make many variants, test them, build on the best.

Problem it solves

Wiele problemów nie ma wzoru na rozwiązanie, a sprawdzenie wszystkich możliwości zajęłoby wieki.

Many problems have no formula for the answer, and checking every possibility would take ages.

What it does

Działa jak hodowca: z każdego pokolenia zostawia najlepsze osobniki, krzyżuje je i lekko zmienia, aż potomstwo jest coraz lepsze.

It works like a breeder: keep the best of each generation, cross them, change them slightly, and offspring keep improving.

Everyday example

Firma kurierska co noc układa trasy setek aut; program próbuje tysięcy wariantów i zachowuje te, które skracają przejazdy.

A courier firm plans routes for hundreds of vans every night; software tries thousands of variants and keeps those that shorten trips.

This concerns you if you…

• umiesz ocenić rozwiązanie, ale nie wiesz, jak je wyprowadzić
• szukasz kilku różnych dobrych wariantów, nie jednego
• chcesz, by program sam wymyślał i ulepszał kod
• can score a solution but cannot derive one
• want several different good options, not just one
• want software that invents and improves code by itself

Subtopics

• Budowa algorytmu ewolucyjnego: reprezentacja, operatory, parametry (Designing an evolutionary algorithm: representation, operators and parameters): Components of an evolutionary algorithm: representation, initialisation, parent and survivor selection, variation operators, constraint handling, parameter control and sound comparison.
  • Krzyżowanie: łączenie cech dwóch rodziców (Crossover (recombination) operators): Recombination operators building offspring from several parents' material, chosen to fit the representation: binary, real-valued or permutation.
  • Mutacja: drobne losowe zmiany rozwiązania (Mutation operators): Unary variation operators adding random perturbations to an individual; they provide exploration and reachability of the whole space.
  • Nisze i utrzymywanie różnorodności populacji (Niching and diversity preservation): Diversity-preserving techniques that let population-based metaheuristics locate multiple optima of a multimodal function in a single run.
  • Obsługa ograniczeń w algorytmach ewolucyjnych (kary, naprawa) (Constraint handling in evolutionary algorithms): Techniques for steering evolutionary search in constrained spaces: penalties, repair, feasibility rules, special operators and multi-objective formulations.
  • Reprezentacja osobnika (kodowanie binarne, liczbowe, permutacje, drzewa) (Solution representation in evolutionary algorithms): The genotype–phenotype mapping: choosing a data structure for candidates that defines the search space and admissible operators.
  • Rzetelne porównywanie algorytmów optymalizacji (benchmarki) (Benchmarking evolutionary and black-box optimisers): Methodology for comparing black-box optimisers: standardised function suites, evaluation budgets, time-to-target measures and statistical tests.
    • Krytyka „nowych” metaheurystyk opartych na metaforach (Critique of metaphor-based metaheuristics): A methodological strand showing many metaphor-based metaheuristics are renamed known techniques, and calling for description in standard optimisation terms.
  • Selekcja rodziców i przeżycia (turniej, ruletka, elitaryzm) (Parent and survivor selection (tournament, roulette, elitism)): Mechanisms for choosing parents and survivors that control selection pressure: proportional, rank-based and tournament, plus replacement strategies and elitism.
  • Strojenie i samoadaptacja parametrów algorytmu ewolucyjnego (Parameter tuning and control in evolutionary algorithms): Parameter tuning (static choice before a run) and parameter control (deterministic, adaptive or self-adaptive changes during a run).
• Ewolucja z modelami językowymi (LLM-guided evolutionary search): Methods where large language models act as variation operators in an evolutionary algorithm while an evaluator automatically scores programs, prompts or agents.
  • Ewolucyjne odkrywanie algorytmów z LLM (LLM-driven program and algorithm evolution): Evolutionary search over programs in which a language model generates code variants and an evaluator scores them on a mathematical or engineering task.
  • Ewolucyjne ulepszanie poleceń dla modeli językowych (Evolutionary prompt optimisation): Optimising prompt text with an evolutionary algorithm where a language model performs mutation and crossover and selection uses validation scores.
  • Model językowy jako operator krzyżowania i mutacji (Language models as evolutionary variation operators): Using a language model as the variation operator in an evolutionary algorithm: mutation by editing or crossover by prompting several parents.
  • Samodoskonalące się agenty (ewolucja agentów) (Evolutionary self-improvement of agents): Evolutionary, archive-based search over agent designs where a foundation model edits agent code and benchmarks empirically validate changes.
• Inteligencja roju (Swarm intelligence): Optimisation methods built on collective behaviour of many simple agents, mainly particle swarms and ant colony algorithms.
  • Algorytmy mrówkowe (ACO) (Ant colony optimisation (ACO)): A metaheuristic for combinatorial problems where agents probabilistically build solutions guided by pheromone and heuristics, with pheromone reinforced and evaporated.
  • Optymalizacja rojem cząstek (PSO) (Particle swarm optimisation (PSO)): A population metaheuristic in which particles update velocity from inertia, their personal best and their neighbourhood best.
  • Robotyka rojowa (Swarm robotics): Designing collective behaviour for large groups of simple autonomous robots with local sensing and communication and no central control.
  • Symulacja stada (boids) (Boids and flocking simulation): A distributed flocking model where each agent steers by three rules relative to neighbours within a local range.
  • Sztuczna kolonia pszczół (ABC) (Artificial bee colony algorithm (ABC)): A swarm metaheuristic with three bee roles — employed, onlooker and scout — for continuous optimisation.
• Klasyczne algorytmy ewolucyjne (Classical evolutionary algorithms): The main evolutionary algorithm families: genetic algorithms, evolution strategies, genetic programming and evolutionary multi-objective optimisation.
  • Algorytmy estymacji rozkładu (EDA) (Estimation of distribution algorithms (EDA)): Probabilistic model-building metaheuristics: they fit a distribution to selected good solutions and sample the next population from it.
  • Algorytmy genetyczne (Genetic algorithms (GA)): A population algorithm encoding solutions as chromosomes and creating offspring through selection, crossover and mutation.
    • Model wyspowy (równoległe populacje z migracją) (Island model and parallel evolutionary algorithms): Coarse-grained parallel evolution: independent subpopulations exchanging individuals according to a migration topology and schedule.
  • Algorytmy memetyczne (ewolucja z lokalnym ulepszaniem) (Memetic algorithms): An evolutionary algorithm with one or more local search phases inside its generational cycle.
  • Ewolucja różnicowa (DE) (Differential evolution (DE)): A population method for continuous optimisation where a mutant is a base vector plus a scaled difference of two random members.
  • Ewolucja wspomagana modelem zastępczym (gdy ocena jest droga) (Surrogate-assisted evolutionary optimisation): Evolutionary algorithms replacing some expensive objective evaluations with approximations from regression or classification models learned during the search.
  • Ewolucyjna optymalizacja wielokryterialna (NSGA-II) (Evolutionary multi-objective optimisation (NSGA-II)): Evolutionary algorithms approximating the Pareto front of a multi-objective problem in one run, such as NSGA-II, NSGA-III and MOEA/D.
    • Optymalizacja przy wielu celach naraz (NSGA-III, MOEA/D) (Many-objective evolutionary optimisation (NSGA-III, MOEA/D)): Evolutionary algorithms for problems with typically four or more objectives, built on reference points or decomposition into scalar subproblems.
    • Wskaźnik hiperobjętości frontu Pareto (Hypervolume indicator for Pareto fronts): A quality indicator for a set of non-dominated solutions, measuring the objective-space volume dominated by that set relative to a reference point.
  • Koewolucja: populacje oceniane przez wzajemne starcia (Coevolutionary algorithms): Evolutionary algorithms in which at least part of the selection signal comes from interactions among individuals in one or more populations.
  • Programowanie genetyczne i regresja symboliczna (Genetic programming and symbolic regression): Evolution of expression trees or programs; its main use is symbolic regression, discovering readable formulas from data.
    • Ewolucja gramatyczna (programy budowane z reguł gramatyki) (Grammatical evolution): Genetic programming with a genotype–phenotype mapping through a context-free grammar in BNF notation, evolving programs in an arbitrary language.
    • Ewolucyjne ulepszanie i naprawa istniejącego kodu (Genetic improvement and automated program repair): Search, usually by genetic programming, over edits to existing code to repair bugs or improve performance.
    • Kartezjańskie programowanie genetyczne (programy jako siatka węzłów) (Cartesian genetic programming (CGP)): Genetic programming encoded as a directed graph on a grid of nodes, with an integer genotype and inactive genes enabling neutral drift.
  • Strategie ewolucyjne i CMA-ES (Evolution strategies and CMA-ES): Evolutionary continuous optimisers that sample candidates from a normal distribution and adapt its mean, step size and covariance matrix.
    • Naturalne strategie ewolucyjne (NES) (Natural evolution strategies (NES)): Evolution strategies that update search-distribution parameters by the natural gradient of expected fitness.
  • Uczące się systemy klasyfikatorów (ewolucja reguł „jeśli–to”) (Learning classifier systems (LCS)): Rule-based learning systems combining a population of condition–action classifiers, reward-based credit assignment and a genetic algorithm discovering new rules.
• Neuroewolucja (Neuroevolution): Applying evolutionary algorithms to optimise weights, architectures, hyperparameters or learning rules of neural networks.
  • Ewolucja budowy sieci (NEAT) (NeuroEvolution of Augmenting Topologies (NEAT)): An algorithm evolving network weights and topology together, using historical gene markings, speciation and gradual complexification from minimal structure.
    • Ewolucja wzorów połączeń dużych sieci (HyperNEAT) (HyperNEAT and compositional pattern-producing networks): Neuroevolution with indirect encoding: NEAT evolves a CPPN that sets connection weights between neurons placed in geometric space.
  • Ewolucyjne projektowanie architektur sieci (NAS) (Evolutionary neural architecture search): Searching neural architecture space with an evolutionary algorithm whose fitness is the quality of the trained candidate.
  • Strategie ewolucyjne w trenowaniu sieci i LLM (Evolution strategies as an alternative to RL): Scalable evolution strategies estimating an improvement direction from scores of randomly perturbed parameter copies, used in RL and in fine-tuning large language models.
  • Trenowanie populacyjne (Population Based Training) (Population Based Training (PBT)): An asynchronous method jointly optimising weights and hyperparameters of a model population, discovering a hyperparameter schedule rather than one fixed setting.
• Różnorodność i ewolucja otwarta (Quality diversity and open-endedness): Algorithms that illuminate a solution space with collections of diverse high-quality candidates, plus research on open-ended, never-ending generation of novelty.
  • MAP-Elites: mapa najlepszych wariantów (MAP-Elites): A quality-diversity algorithm keeping a grid of niches defined by behaviour features and the best solution in each niche.
  • Poszukiwanie nowości (novelty search) (Novelty search): An evolutionary algorithm replacing the objective with a behavioural novelty score relative to population and archive, effective on deceptive problems.
  • Red teaming jako wyszukiwanie quality-diversity (Quality-diversity red teaming): Generating diverse adversarial prompts as a quality-diversity problem: a MAP-Elites archive indexed by risk category and attack style.
  • Systemy otwarte (open-endedness) (Open-ended learning systems): Research on systems that endlessly generate novel, learnable artefacts or tasks along with their solutions.

Sources

• GECCO 2026 – program tracks (ACM SIGEVO) · ACM SIGEVO
• Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap · Xingyu Wu, Sheng-hao Wu, Jibin Wu, Liang Feng, Kay Chen Tan
• Designing neural networks through neuroevolution · Kenneth O. Stanley, Jeff Clune, Joel Lehman, Risto Miikkulainen