Memetic algorithms (MAs) represent one of the recent growing areas in evolutionary algorithm (EA) research. The term MAs is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian EAs, Lamarckian EAs, cultural algorithms, or genetic local searches. In the last decade, MAs have been demonstrated to converge to high-quality solutions more efficiently than their conventional counterparts on a wide range of real-world problems. Despite the success and surge in interests on MAs, many of the successful MAs reported have been crafted to suit problems in very specific domains. Given the restricted theoretical knowledge available in the field of MAs and the limited progress made on formal MA frameworks, we present a novel probabilistic memetic framework that models MAs as a process involving the decision of embracing the separate actions of evolution or individual learning and analyzing the probability of each process in locating the global optimum. Further, the framework balances evolution and individual learning by governing the learning intensity of each individual according to the theoretical upper bound derived while the search progresses. Theoretical and empirical studies on representative benchmark problems commonly used in the literature are presented to demonstrate the characteristics and efficacies of the probabilistic memetic framework. Further, comparisons to recent state-of-the-art evolutionary algorithms, memetic algorithms, and hybrid evolutionary-local search demonstrate that the proposed framework yields robust and improved search performance.

The recent spread of African swine fever (ASF) in the People's Republic of China and neighbouring countries in Asia has had significant economic consequences with an estimated direct cost of $55-$130 billion. This pandemic, originally detected in Republic of Georgia in 2007, has devastated the swine industry in large geographical areas of Southeast Asia with 14 countries reporting ASF outbreaks since the first documented case was confirmed in the city of Shenyang, Liaoning Province, China, on 3 August 2018. In the absence of any available vaccines, the control of ASF relies on the detection and culling of infected animals. The United States Department of Agriculture recently developed a recombinant experimental vaccine candidate, ASFV-G-ΔI177L, by deleting the I177L gene from the genome of the highly virulent pandemic ASFV strain Georgia, which efficaciouly protects pigs from the parental virus. Here, the initial studies were extended demonstrating that ASFV-G-ΔI177L is able to protect pigs against the virulent ASFV isolate currently circulating and producing disease in Vietnam with similar efficacy as reported against the Georgia strain. Comparative studies performed using a large number of pigs of European and Vietnamese origin demonstrated that a minimum protective dose of 10 2 HAD 50 of ASFV-G-ΔI177L equally protects animals of both breeds. In concurrence with those results, the onset of immunity in these animal breed showed appearance of protection in approximately one-third of the animals by the second week post vaccination, with full protection achieved by the fourth week post vaccination. Therefore, results presented here demonstrated that ASFV-G-ΔI177L is able to induce protection against virulent Vietnameese ASFV field strains and is effective in protecting local breeds of pigs as efficiently as previously shown for European cross-bred pigs. To our knowledge, this is the first report showing the efficacy of a Georgia 2007 based vaccine candidate in Asian breed of pigs or challenged with an Asian ASFV strain.

At present, the successful transmission of drug‐resistant Mycobacterium tuberculosis, including multidrug‐resistant (MDR) and extensively drug‐resistant (XDR) strains, in human populations, threatens tuberculosis control worldwide. Differently from many other bacteria, M. tuberculosis drug resistance is acquired mainly through mutations in specific drug resistance‐associated genes. The panel of mutations is highly diverse, but depends on the affected gene and M. tuberculosis genetic background. The variety of genetic profiles observed in drug‐resistant clinical isolates underlines different evolutionary trajectories towards multiple drug resistance, although some mutation patterns are prominent. This review discusses the intrinsic processes that may influence drug resistance evolution in M. tuberculosis, such as mutation rate, drug resistance‐associated mutations, fitness cost, compensatory mutations and epistasis. This knowledge should help to better predict the risk of emergence of highly resistant M. tuberculosis strains and to develop new tools and strategies to limit the development and spread of MDR and XDR strains.