GP is an evolutionary algorithm EA working with a population of programs, represented as combinatorial structures AST-like trees, instruction sequences, graphs - depending on the 'genre' of GP. In each iteration, programs in the population are evaluated assessed how well they realize the required functionality , and the well-performing ones are selected and modified using search operators mutated and crossed-over. In this process, the quality of programs tends to gradually improve and ultimately the sought program is usually synthesized after a number of generations.
Op , which follows the tree-based paradigm of GP: programs are expression trees, where inner tree nodes typically represent instructions, while tree leaves fetch input data e. This paradigm is most convenient for evolving side effect-free expressions, but in SWIM it can be also used with imperative programs with side-effects. The space of feasible solutions syntactically correct programs is defined by a grammar Grammar case class.
A grammar is a list of productions that define all permissible ways in which programs can be constructed. Building grammars in SWIM is straightforward; an example of a grammar for simple arithmetic expressions, with one input variable x and three constants:. Grammars are essential to generate syntactically correct candidate programs and to manipulate them in a way that preserves their syntactic correctness.
SWIM can be used for single-type problems too; in such cases, the grammar hosts only one nonterminal symbol, the starting symbol of the grammar. This is the most popular mode of operation of GP, used in symbolic regression app. Regression , synthesis of Boolean functions app. Boolean , and some other domains e.
Examples of multi-type problems included in SWIM are: the toy problem of synthesizing a program that calculates the maximum of a pair of numbers app. Min2 and synthesizing a program that determines the position of an integer in a sorted array app. Grammars define only program syntax.
The semantics of particular instructions are defined in a separate class, which should implement the Domain trait. A domain works as program interpreter, which for tree-based GP can be conveniently implemented using recursion and pattern matching see app.
Krzysztof Krawiec: Behavioral program synthesis with genetic programming
MinDomain for an example. Or, discuss a change on Slack. Parent task if any : Description with markdown optional :. Higher is better for the metric. Uses extra training data. Data evaluated on. Program Synthesis Edit. State-of-the-art leaderboards Add a result. No evaluation results yet. Help compare methods by submit evaluation metrics.
We explore this new mutation operator and other well-performing high-rate mutation schemes to determine what traits are crucial to improved performance. We present SignalGP, a new genetic programming GP technique designed to incorporate the event-driven programming paradigm into computational evolution's toolbox.
Event-driven programming is a software design philosophy that simplifies the development of reactive programs by automatically triggering program modules event-handlers in response to external events, such as signals from the environment or messages from other programs. SignalGP incorporates these concepts by extending existing tag-based referencing techniques into an event-driven context.
Both events and functions are labeled with evolvable tags; when an event occurs, the function with the closest matching tag is triggered. We demonstrate the value of the event-driven paradigm using two distinct test problems an environment coordination problem and a distributed leader election problem by comparing SignalGP to variants that are otherwise identical, but must actively use sensors to process events or messages. In each of these problems, rapid interaction with the environment or other agents is critical for maximizing fitness.
When search operators in genetic programming GP insert new instructions into programs, they usually draw them uniformly from the available instruction set. Prefering some instructions to others would require additional domain knowledge, which is typically unavailable.
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However, it has been recently demonstrated that the likelihoods of instructions' occurrence in a program can be reasonably well estimated from its input-output behavior using a neural network. We exploit this idea to bias the choice of instructions used by search operators in GP.
Given a large sample of programs and their input-output behaviors, a neural network is trained to predict the presence of individual instructions. When applied to a new program synthesis task, the network is first queried on the set of examples that define the task, and the obtained probabilities determine the frequencies of using instructions in initialization and mutation operators. This priming leads to significant improvements of the odds of successful synthesis on a range of benchmarks.
Advances in Geometric Semantic Genetic Programming GSGP have shown that this variant of Genetic Programming GP reaches better results than its predecessor for supervised machine learning problems, particularly in the task of symbolic regression. However, by construction, the geometric semantic crossover operator generates individuals that grow exponentially with the number of generations, resulting in solutions with limited use. GSGP-Red works by expanding the functions generated by the geometric semantic operators. The resulting expanded function is guaranteed to be a linear combination that, in a second step, has its repeated structures and respective coefficients aggregated.
Experiments in 12 real-world datasets show that it is not only possible to create smaller and completely equivalent individuals in competitive computational time, but also to reduce the number of nodes composing them by 58 orders of magnitude, on average.
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Genetic programming has been considered as a powerful approach to automated design of production scheduling heuristics in recent years. Flexible and variable representations allow genetic programming to discover very competitive scheduling heuristics to cope with a wide range of dynamic production environments. However, evolving sophisticated heuristics to handle multiple scheduling decisions can greatly increase the search space and poses a great challenge for genetic programming.
To tackle this challenge, a new genetic programming algorithm is proposed to incrementally construct the map of explored areas in the search space and adaptively guide the search towards potential heuristics.
In the proposed algorithm, growing neural gas and principal component analysis are applied to efficiently generate and update the map of explored areas based on the phenotypic characteristics of evolved heuristics. Based on the obtained map, a surrogate assisted model will help genetic programming determine which heuristics to be explored in the next generation. When applied to evolve scheduling heuristics for dynamic flexible job shop scheduling problems, the proposed algorithm shows superior performance as compared to the standard genetic programming algorithm.