Many-objective optimization has posed a great challenge to the classical Pareto dominance-based multiobjective evolutionary algorithms (MOEAs). In this paper, an evolutionary algorithm based on a new dominance relation is proposed for many-objective optimization. The proposed evolutionary algorithm aims to enhance the convergence of the recently suggested nondominated sorting genetic algorithm III by exploiting the fitness evaluation scheme in the MOEA based on decomposition, but still inherit the strength of the former in diversity maintenance. In the proposed algorithm, the nondominated sorting scheme based on the introduced new dominance relation is employed to rank solutions in the environmental selection phase, ensuring both convergence and diversity. The proposed algorithm is evaluated on a number of well-known benchmark problems having 3–15 objectives and compared against eight state-of-the-art algorithms. The extensive experimental results show that the proposed algorithm can work well on almost all the test functions considered in this paper, and it is compared favorably with the other many-objective optimizers. Additionally, a parametric study is provided to investigate the influence of a key parameter in the proposed algorithm.

Computers and artificial intelligence play a key role in the production of artwork through the designing of synthetic agents that are able to reproduce the capabilities of human artists in assembling high-quality artefacts such as paintings and sculptures. In this context, music composition represents one of the art disciplines that can greatly benefit from the appropriate use of computational intelligence, as witnessed by the large number of research activities performed in this field over the recent years. Nevertheless, the automatic composition of music is far from being completely and precisely perfected due to the intrinsic virtuosity that characterizes human musicians’ capabilities. This paper reduces this gap with the proposal of an intelligent scheme for the efficient composition of melodies based on a musical method that is inspired by and strongly characterized by human virtuosity: the unfigured bass technique. In particular, we formulate this music composition technique as an optimization problem and solve it with an adaptive multiagent memetic approach comprising diverse metaheuristics, the composer agents that cooperate to create high-quality four-voice pieces of music starting from a bass line as input. A collection of experimental studies on the famous Bach’s four-voice chorales showed that the cooperation among different optimization strategies yields improved performance over the solutions obtained by conventional and hybrid evolutionary algorithms.

The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while covariance matrix adaptation evolution strategy (CMA-ES) is one of the most efficient algorithms for unconstrained optimization problems, it cannot be readily applied to constrained ones. Here, we used concepts from memetic computing, i.e., the harmonious combination of multiple units of algorithmic information, and viability evolution, an alternative abstraction of artificial evolution, to devise a novel approach for solving optimization problems with inequality constraints. Viability evolution emphasizes the elimination of solutions that do not satisfy viability criteria, which are defined as boundaries on objectives and constraints. These boundaries are adapted during the search to drive a population of local search units, based on CMA-ES, toward feasible regions. These units can be recombined by means of differential evolution operators. Of crucial importance for the performance of our method, an adaptive scheduler toggles between exploitation and exploration by selecting to advance one of the local search units and/or recombine them. The proposed algorithm can outperform several state-of-the-art methods on a diverse set of benchmark and engineering problems, both for quality of solutions and computational resources needed.

Evolutionary Computation, Ahead of Print.

GPEM 16(4) is now available. It contains 4 original papers:

“A learning automata-based memetic algorithm”
by M. Rezapoor Mirsaleh and M. R. Meybodi
doi:10.1007/s10710-015-9241-9

“Controlling code growth by dynamically shaping the genotype size distribution”
by Marc-Andre Gardner and Christian Gagne and Marc Parizeau
doi:10.1007/s10710-015-9242-8

“Prudent alignment and crossover of decision trees in genetic programming”
Matej Sprogar
doi:10.1007/s10710-015-9243-7

“Neutral genetic drift: an investigation using Cartesian Genetic Programming”
Andrew Turner and Julian Miller
doi:10.1007/s10710-015-9244-6

Finally we finish 2015 with Leonardo Trujillo’s review of
Ken Stanley and Joel Lehman’s book
“Why greatness cannot be planned: the myth of the objective”
doi:10.1007/s10710-015-9250-8
(Remember book reviews are now free to download:-)

As you would expect;-) these are now included in the GP bibliography
Bill

GPEM 16(4) is now available. It contains 4 original papers:

“A learning automata-based memetic algorithm”
by M. Rezapoor Mirsaleh and M. R. Meybodi
doi:10.1007/s10710-015-9241-9

“Controlling code growth by dynamically shaping the genotype size distribution”
by Marc-Andre Gardner and Christian Gagne and Marc Parizeau
doi:10.1007/s10710-015-9242-8

“Prudent alignment and crossover of decision trees in genetic programming”
Matej Sprogar
doi:10.1007/s10710-015-9243-7

“Neutral genetic drift: an investigation using Cartesian Genetic Programming”
Andrew Turner and Julian Miller
doi:10.1007/s10710-015-9244-6

Finally we finish 2015 with Leonardo Trujillo’s review of
Ken Stanley and Joel Lehman’s book
“Why greatness cannot be planned: the myth of the objective”
doi:10.1007/s10710-015-9250-8
(Remember book reviews are now free to download:-)

As you would expect;-) these are now included in the GP bibliography
Bill