Grammatical evolution (GE) has proven effective at program synthesis. However, its performance is hindered by the exponential growth of search spaces as grammar complexity increases. Methods utilizing recent advances in large language models and genetic programming have outperformed GE on some benchmarks, affirming the need for scalable approaches. This paper addresses the scalability challenge by proposing an in-context learning method to automatically generate minimal specialized grammars (MSGs), which are problem-specific subsets of a larger grammar designed to reduce search space complexity. To the best of our knowledge, this represents the first use of in-context learning for program synthesis within GE. Our approach conditions a language model on examples of Backus-Naur Form grammars to generate MSGs tailored to individual synthesis tasks. We evaluate this framework on a benchmark suite widely used in GE research. Experimental results show our method almost always outperforms the baseline GE approach, improving both the number and frequency of problems solved while reducing computational cost as measured by fitness evaluations.

Combining a surrogate model and a heuristic-based optimizer for multi-objective optimization is now a common approach to make best use of the available computational budget. One possible combination is to use a local surrogate that acts as a guide for local search as a module of the heuristic algorithm. The local search works by optimizing the scalarizing function and uses the local surrogate as a cheap replacement of the original function. Various scalarizing functions exist and an understanding of the advantages and disadvantages of these functions is needed for further improvement of the optimization algorithms. In this paper, various scalarizing functions implemented inside a single surrogate assisted local search memetic algorithm (SS-MOMA) framework are compared. The scalarizing functions studied here are the Tchebycheff type (SS-MOMA-TC) and weighted sum (SS-MOMA-WS) with 15-dimensional ZDT1, ZDT2, and ZDT3 test problems as the benchmark problems using the generational distance and diversity metrics as performance indicators. On the ZDT1, ZDT2, and ZDT3 problems, SS-MOMA-TC clearly outperforms SS-MOMA-WS. The results show that the Tchebycheff scalarizing function can enhance the diversity of the non-dominated solutions independent of the convexity of the problem, but it encounters a slight difficulty with the discontinuous Pareto front of ZDT3.

The Multi-objective Alliance Algorithm (MOAA), a recently introduced optimization algorithm, is used for the optimization of heterogeneous low-enriched uranium (LEU) + mixed-oxide fuel (MOX) assemblies for pressurized water reactors (PWRs). This is a constrained nuclear problem with two objectives and a mixed-integer solution space. The efficacy of the algorithm is demonstrated through comparisons with NSGA-II for between 300 and 2000 function evaluations. Through the epsilon and hypervolume indicators and the Kruskal-Wallis statistical test, we show that the MOAA outperforms NSGA-II on this problem. The MOAA was also able to find a set of solutions that are better than the ‘expert solution’ for this problem.

This paper introduces a new version of the multiobjective Alliance Algorithm (MOAA) applied to the optimization of the NACA 0012 airfoil section, for minimization of drag and maximization of lift coefficients, based on eight section shape parameters. Two software packages are used: XFoil which evaluates each new candidate airfoil section in terms of its aerodynamic efficiency, and a Free-Form Deformation tool to manage the section geometry modifications. Two versions of the problem are formulated with different design variable bounds. The performance of this approach is compared, using two indicators and a statistical test, with that obtained using NSGA-II and multi-objective Tabu Search (MOTS) to guide the optimization. The results show that the MOAA outperforms MOTS and obtains comparable results with NSGA-II on the first problem, while in the other case NSGA-II is not able to find feasible solutions and the MOAA is able to outperform MOTS.

A baseline NACA0012 two-dimensional (2D) airfoil is optimized for supersonic flight conditions using a recently introduced optimization algorithm: the multi-objective alliance algorithm (MOAA). The efficacy of the algorithm is demonstrated through comparisons with NSGA-II for 300, 600 and 1000 function evaluations. Through epsilon/hypervolume indicators and the Mann-Whitney statistical test, we show that MOAA outperforms NSGA-II on this problem.

This paper presents a preliminary study which describes and evaluates a multi-objective (MO) version of a recently created single objective (SO) optimization algorithm called the “Alliance Algorithm” (AA). The algorithm is based on the metaphorical idea that several tribes, with certain skills and resource needs, try to conquer an environment for their survival and to ally together to improve the likelihood of conquest. The AA has given promising results in several fields to which has been applied, thus the development of a MO variant (MOAA) is a natural extension. Here the MOAA's performance is compared with two well-known MO algorithms: NSGA-II and SPEA-2. The performance measures chosen for this study are the convergence and diversity metrics. The benchmark functions chosen for the comparison are from the ZDT and OKA families and the main classical MO problems. The results show that the three algorithms have similar overall performance. Thus, it is not possible to identify a best algorithm for all the problems; the three algorithms show a certain complementarity because they offer superior performance for different classes of problems.

This paper introduces a new technique called species conservation for evolving parallel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current generation are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.

This paper introduces a new technique called species conservation for evolving paral-lel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current gen-eration are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimiza-tion problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.

This paper describes how the species conservation genetic algorithm (SCGA) (Balazs et at., 2001) can be adapted to practical problems by using domain specific distance definitions to measure the dissimilarity between elements of the search space.

This paper examines the distributions of the species seeds in the species conservation genetic algorithm and analyzes the complexity of the overhead introduced by species conservation. The results show that the overhead required by the species conservation technique is no worse than that introduced by any other parallel subpopulation technique.