Mycogen Seeds

Mycogen Seeds, headquartered in Indianapolis, Indiana, United States, provides seeds for agriculture. Mycogen is one of the largest sunflower seed producers.[citation needed] Mycogen produces, markets and sells hybrid seed corn. The company also markets and sells sorghum, sunflower, soybean, alfalfa, and canola.

The Mycogen Corporation was formed in 1982 by members of the San Diego business and scientific communities, including David H. Rammler, a partner in the venture capital firm of Vanguard Associates, who served as the first chairman of the company, and Andrew C. Barnes, a biochemist with an MBA from the Stanford School of Business. The original concept was to develop environmentally safe herbicides from fungi using genetic engineering, thus the name Mycogen, coined from the Greek words for fungus and genetics.[citation needed]

The development of MAs

1st generation

The first generation of MA refers to hybrid algorithms, a marriage between a population-based global search (often in the form of an evolutionary algorithm) coupled with a cultural evolutionary stage. This first generation of MA although encompasses characteristics of cultural evolution (in the form of local refinement) in the search cycle, it may not qualify as a true evolving system according to Universal Darwinism, since all the core principles of inheritance/ memetic transmission, variation and selection are missing. This suggests why the term MA stirred up criticisms and controversies among researchers when first introduced in The Selfish Gene.


2nd generation

Multi-meme , Hyper-heuristic and Meta-Lamarckian MA are referred to as second generation MA exhibiting the principles of memetic transmission and selection in their design. In Multi-meme MA, the memetic material is encoded as part of the genotype. Subsequently, the decoded meme of each respective individual / chromosome is then used to perform a local refinement. The memetic material is then transmitted through a simple inheritance mechanism from parent to offspring(s). On the other hand, in hyper-heuristic and meta-Lamarckian MA, the pool of candidate memes considered will compete, based on their past merits in generating local improvements through a reward mechanism, deciding on which meme to be selected to proceed for future local refinements. Memes with a higher reward have a greater chance of being replicated or copied. For a review on second generation MA, i.e., MA considering multiple individual learning methods within an evolutionary system, the reader is referred to .

3rd generation

Co-evolution and self-generation MAs may be regarded as 3rd generation MA where all three principles satisfying the definitions of a basic evolving system has been considered. In contrast to 2nd generation MA which assumes the pool of memes to be used being known a priori, a rule-based representation of local search is co-adapted alongside candidate solutions within the evolutionary system, thus capturing regular repeated features or patterns in the problem space.

Some design notes
The frequency and intensity of individual learning directly define the degree of evolution (exploration) against individual learning (exploitation) in the MA search, for a given fixed limited computational budget. Clearly, a more intense individual learning provides greater chance of convergence to the local optima but limits the amount of evolution that may be expended without incurring excessive computational resources. Therefore, care should be taken when setting these two parameters to balance the computational budget available in achieving maximum search performance. When only a portion of the population individuals undergo learning, the issue on which subset of individuals to improve need to be considered to maximize the utility of MA search. Last but not least, the individual learning procedure/meme used also favors a different neighborhood structure, hence the need to decide which meme or memes to use for a given optimization problem at hand would be required.

How often should individual learning be applied?
One of the first issues pertinent to memetic algorithm design is to consider how often the individual learning should be applied, i.e., individual learning frequency. In the effect of individual learning frequency on MA search performance was considered where various configurations of the individual learning frequency at different stages of the MA search were investigated. Conversely, it was shown in that it may be worthwhile to apply individual learning on every individual if the computational complexity of the individual learning is relatively low.

On which solutions should individual learning be used?
On the issue of selecting appropriate individuals among the EA population that should undergo individual learning, fitness-based and distribution-based strategies were studied for adapting the probability of applying individual learning on the population of chromosomes in continuous parametric search problems with Land extending the work to combinatorial optimization problems. Bambha et al. introduced a simulated heating technique for systematically integrating parameterized individual learning into evolutionary algorithms to achieve maximum solution quality.

Memetic algorithm

Memetic algorithms (MA) represent one of the recent growing areas of research in evolutionary computation. The term MA 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, MA are also referred to in the literature as Baldwinian EAs, Lamarckian EAs, cultural algorithms or genetic local search.

Introduction
The theory of “Universal Darwinism” was coined by Richard Dawkins in 1983 to provide a unifying framework governing the evolution of any complex system. In particular, “Universal Darwinism” suggests that evolution is not exclusive to biological systems, i.e., it is not confined to the narrow context of the genes, but applicable to any complex system that exhibit the principles of inheritance, variation and selection, thus fulfilling the traits of an evolving system. For example, the new science of memetics represents the mind-universe analogue to genetics in culture evolution that stretches across the fields of biology, cognition and psychology, which has attracted significant attention in the last decades. The term “meme” was also introduced and defined by Dawkins [1] in 1976 as “the basic unit of cultural transmission, or imitation”, and in the English Oxford Dictionary as “an element of culture that may be considered to be passed on by non-genetic means”.

Inspired by both Darwinian principles of natural evolution and Dawkins’ notion of a meme, the term “Memetic Algorithm” (MA) was first introduced by Moscato in his technical report in 1989 where he viewed MA as being close to a form of population-based hybrid genetic algorithm (GA) coupled with an individual learning procedure capable of performing local refinements. The metaphorical parallels, on the one hand, to Darwinian evolution and, on the other hand, between memes and domain specific (local search) heuristics are captured within memetic algorithms thus rendering a methodology that balances well between generality and problem specificity. In a more diverse context, memetic algorithms are now used under various names including Hybrid Evolutionary Algorithms, Baldwinian Evolutionary Algorithms, Lamarckian Evolutionary Algorithms, Cultural Algorithms or Genetic Local Search. In the context of complex optimization, many different instantiations of memetic algorithms have been reported across a wide range of application domains, in general, converging to high quality solutions more efficiently than their conventional evolutionary counterparts.

In general, using the ideas of memetics within a computational framework is called "Memetic Computing" (MC). With MC, the traits of Universal Darwinism are more appropriately captured. Viewed in this perspective, MA is a more constrained notion of MC. More specifically, MA covers one area of MC, in particular dealing with areas of evolutionary algorithms that marry other deterministic refinement techniques for solving optimization problems. MC extends the notion of memes to cover conceptual entities of knowledge-enhanced procedures or representations.

Jasmati

Jasmati Rice is a genetically-engineered hybrid long grain of rice whose name is derived from Jasmine rice and Basmati. It is said to possess the traits of both grains - namely the softness (when cooked) of Basmati, and the nutty aroma of Jasmine - the latter in muted tones so as to be more subtle. Whereas Jasmine Rice is not as widely sold in the average American supermarket, Jasmati has become a more common find.

It appears to have been first created in the United States as a way of capitalizing upon the market successes of both Jasmine and Basmati rice, the patents of which were held by farmers in Thailand and India, respectively.[dubious – discuss] To what degree Jasmati is derived from either of its etymological parent grains is unknown and highly disputed. The patent for Jasmati, registered in 1993 by the Texas-based corporation, Ricetec, created had many legal implications for Thai and Indian farmers who rely heavily on the exports of the parent crops, and proceeded to cause considerable controversy.

Presently the debate seems to have simmered. As the degree to which Jasmati draws from Jasmine cannot be ascertained, the informed consumer should be aware that it is therefore a different grain and may or may not be a complete substitute for either Jasmine or Basmati. This also suggests that Jasmati may have its own unique merits as a cooking ingredient