The field of evolutionary optimization is witnessing significant advancements in efficiency enhancements. Researchers are focusing on developing innovative methods to improve the performance of optimization algorithms, particularly in terms of reducing the number of evaluations required to reach high-quality solutions. One notable direction is the incorporation of incremental learning techniques, which enable algorithms to adapt to changing problem landscapes more effectively. Another area of interest is the development of adaptive estimation methods for determining the required number of algorithm runs, which can lead to substantial reductions in computational resources and environmental impact. Running-time analysis is also receiving attention, with new approaches being proposed to estimate the running time of multi-objective evolutionary algorithms and evolutionary combinatorial optimization algorithms. Noteworthy papers in this regard include: The paper on incremental distribution estimation in RV-GOMEA, which demonstrates a reduction in the required number of evaluations by a factor of up to 1.5. The paper on adaptive estimation of the number of algorithm runs, which achieves an accuracy of 82-95% in estimations and allows for a reduction of approximately 50% in the number of runs.